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Hany= u LiProfessor of Mathematics and Statisti= cs, Chongqing University=E5=9C=A8 cqu.edu.cn =E7=9A=84=E7=94=B5=E5=AD=90=E9=82=AE=E4=BB=B6=E7=BB= =8F=E8=BF=87=E9=AA=8C=E8=AF=81
Kai QiChongqing Normal University=E5=9C=A8 cqnu.edu.cn =E7=9A=84=E7=94=B5=E5=AD=90=E9=82=AE=E4=BB=B6= =E7=BB=8F=E8=BF=87=E9=AA=8C=E8=AF=81
Xiaochao XiaPhD
Dr. Ilyas AliUniversity of Engineering and Technology Lahore, Faisala= bad Campus=E5=9C=A8 uet= .edu.pk =E7=9A=84=E7=94=B5=E5=AD=90=E9=82=AE=E4=BB=B6=E7=BB=8F=E8=BF=87=E9= =AA=8C=E8=AF=81=
Lixing ZhuProfessor of Statistics, Beijing Normal Universi= ty=E5=9C=A8 bnu.edu.cn = =E7=9A=84=E7=94=B5=E5=AD=90=E9=82=AE=E4=BB=B6=E7=BB=8F=E8=BF=87=E9=AA=8C=E8= =AF=81
Shaoxin WangQufu Norm= al University=E5=9C=A8 = qfnu.edu.cn =E7=9A=84=E7=94=B5=E5=AD=90=E9=82=AE=E4=BB=B6=E7=BB=8F=E8=BF=87= =E9=AA=8C=E8=AF=81
Yuehan YangCentral University of Finance and Economics=E5=9C=A8 cufe.edu.cn =E7=9A=84=E7=94=B5=E5=AD=90=E9= =82=AE=E4=BB=B6=E7=BB=8F=E8=BF=87=E9=AA=8C=E8=AF=81
Dr. = Abdul ShakoorDepartment of Mathematics, I= UB, Pakistan & College of Mathematics, Chongqing University, China=E5=9C=A8 cqu.edu.cn =E7=9A= =84=E7=94=B5=E5=AD=90=E9=82=AE=E4=BB=B6=E7=BB=8F=E8=BF=87=E9=AA=8C=E8=AF=81=

=E5=85=B3=E6=B3=A8

Hu Yang=EF=BC=88=E6=9D=A8=E8=99=8E=EF=BC=89

College of Mathematics and Statistics= =EF=BC=8CChongq= ing University

=E5=9C= =A8 cqu.edu.cn =E7=9A=84=E7=94=B5=E5=AD=90=E9=82=AE=E4=BB=B6=E7=BB=8F=E8=BF= =87=E9=AA=8C=E8=AF=81 - =E9=A6=96=E9=A1=B5

linear model financial statistics= support vector machine high dime= nsional model selection matrix algebra

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= Com= munication-Efficient l_0 Penalized Least Square

C= Gong, H Yang

arXiv preprint arXiv:2504.00722, 2025

=E6= =80=80=E5=BF=B5=E7=8E=8B=E6=9D=BE=E6=A1=82=E6=95=99=E6=8E=88

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<= div class=3D"gs_gray">=E5=BA=94=E7=94=A8=E6=A6=82=E7=8E=87=E7=BB=9F=E8=AE= =A1 41 (3), 482-492, 2025<= tr class=3D"gsc_a_tr"><= /tr>= 16<= td class=3D"gsc_a_y">2022<= tr class=3D"gsc_a_tr"><= td class=3D"gsc_a_c">2= 020 2019 = <= tr class=3D"gsc_a_tr"><= td class=3D"gsc_a_t">Model selection consistency of lasso for em= pirical data

Y Yang, H Yang

Chinese Annals of Mathematics, Series B 39 (4), 607-620, 2018

= Estimation and variable selection in single-= index composite quantile regression

H Liu, H Yang=

Communications in Statistics-Simulation and Co= mputation 46 (9), 7022-7039, 2017

<= td class=3D"gsc_a_c">11= Robust variable selection for generalized li= near models with a diverging number of parameters

C Guo, H Yang, J Lv

Communications in Statisti= cs-Theory and Methods 46 (6), 2967-2981, 2017=

<= tr class=3D"gsc_a_tr"><= tr class=3D"gsc_a_tr">2= <= tr class=3D"gsc_a_tr"><= td class=3D"gsc_a_c">8<= /tr><= /tr>2015<= td class=3D"gsc_a_y">2015A robust and efficient estimation method f= or single-index varying-coefficient models

H Yang= , C Guo, J Lv

Statistics & Probability Lett= ers 94, 119-127, 2014

= <= /tr>Generalized norms inequalities for absolut= e value operators

I Ali, H Yang, A Shakoor

<= div class=3D"gs_gray">International Journal of Analysis and Applications 5 = (1), 1-9, 2014Efficiency of an almost unbiased two-param= eter estimator in linear regression model

J Wu, H= Yang

Statistics 47 (3), 535-545, 2013

= 21= <= td class=3D"gsc_a_c"><= tr class=3D"gsc_a_tr"><= tr class=3D"gsc_a_tr"><= tr class=3D"gsc_a_tr"><= tr class=3D"gsc_a_tr">Further results on the group inverses and = Drazin inverses of anti-triangular block matrices

X Liu, H Yang

Applied Mathematics and Computat= ion 218 (17), 8978-8986, 2012

= = = An expression of the general common least-= squares solution to a pair of matrix equations with applications

X Liu, H Yang

Computers & M= athematics with Applications 61 (10), 3071-3078, 201= 1

<= tr class=3D"gsc_a_tr">= 86= = <= tr class=3D"gsc_a_tr"><= tr class=3D"gsc_a_tr"><= tr class=3D"gsc_a_tr">Improvement of the Liu estimator in weight= ed mixed regression

H Yang, X Chang, D Liu

<= div class=3D"gs_gray">Communications in Statistics=E2=80=94Theory and Metho= ds 38 (2), 285-292, 2009= <= /tr>2008= 2008= Classification algorithms based on Fisher = discriminant and perceptron neural network

H Yang= , J Xu

International Symposium on Neural Networ= ks, 20-25, 2005

=E7=BA=BF=E6=80=A7=E5=9B=9E=E5=BD=92=E6=A8= =A1=E5=9E=8B=E4=B8=AD AUL =E4=BC=B0=E8=AE=A1=E4=B8=8E OLS =E4=BC=B0=E8=AE= =A1=E5=8F=8A AURR =E4=BC=B0=E8=AE=A1=E7=9A=84=E5=9D=87=E6=96=B9=E8=AF=AF=E5= =B7=AE=E9=98=B5=E6=AF=94=E8=BE=83

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Enhanced unsupervise= d domain adaptation through joint constrained classifier and feature learni= ng Z Tan, H Yang Engi= neering Applications of Artificial Intelligence 163, 113047, 2026		2026
Bias-enhanced support detection and root finding app= roach H Ming, H Yang = Statistics and Computing 35 (5), 162, 2025		2025
A novel twin parametric-margin support vector machine with capped asymmet= ric elastic net loss J Fu, H Yang Neural Networks, 107998, 2025		2025
A new truncated non-convex loss based support vector machine for robust b= inary classification F Li, K Qi, H Yang Applied Intelligence 55 (13), 906= , 2025		2025
Deterministic Sensitivity Analysis of the Factor Score Esti= mation in the Approximate Factor Model S Wang, B = Guo, H Yang Communications in Mathematics and S= tatistics, 1-18, 2025		2025
Valley-loss multiple = birth support vector machine for multi-class classification X Li, J Zhang, H Yang Neural Compu= ting and Applications 37 (19), 13371-13389, 2025		2025
Distributed algorithm for best subset regression H Ming, H Yang Expert Systems with Applic= ations 277, 127224, 2025		2025
Reweighted angl= e two-dimensional principal component analysis for feature extractionZ Tan, H Yang Pattern Reco= gnition, 111817, 2025	3	2025
Robust support vector machine based on the = bounded asymmetric least squares loss function and its applications in nois= e corrupted data J Zhang, H Yang Advanced Engineering Informatics 65, 103371, 2025	2	2025
<= /a>	2025<= /span>
Non-negative Constrained Penalty for = High-Dimensional Correlated Data H Ming, Y Chen, = H Yang Communications in Mathematics and Statis= tics, 1-20, 2025		2025
L₀-regul= arized high-dimensional sparse multiplicative models H Ming, H Yang, X Xia Statistical Theory an= d Related Fields 9 (1), 59-83, 2025		2025
	2025
A novel bounded= loss framework for support vector machines F Li,= H Yang Neural Networks 178, 106476, 2024	8	2024
Group sparse structural smoothing recovery: model, statistical proper= ties and algorithm Z Tan, H Yang Statistics and Computing 34 (4), 122, 2= 024	2	2024
Efficient = variable selection for high-dimensional multiplicative models: a novel LPRE= -based approach Y Chen, H Ming, H Yang Statistical Papers 65 (6), 3713-3737, 2024	3	2024
Bounded= quantile loss for robust support vector machines-based classification and = regression J Zhang, H Yang Expert Systems with Applications 242, 122759, = 2024	19	2024
L0 regula= rized logistic regression for large-scale data H = Ming, H Yang Pattern Recognition 146, 110024, 2024	16	2024
Fused robust geometric nonparallel hyperplane support vecto= r machine for pattern classification R Gao, K Qi,= H Yang Expert Systems with Applications 236, 1= 21331, 2024	<= a href=3D"https://scholar.google.com/scholar?oi=3Dbibs&hl=3Dzh-CN&c= ites=3D109830991256960239" class=3D"gsc_a_ac gs_ibl">9	2024
A fast robust best subset regression H Ming, H Yang Knowledge-Based Systems 28= 4, 111309, 2024	8	2024
Total variation r= egularized multi-matrices weighted Schatten p-norm minimization for image d= enoising Z Tan, H Yang Applied Mathematical Modelling 124, 518-531, 2023<= /span>	18	2023
Group sparse recovery via group square-root elastic n= et and the iterative multivariate thresholding-based algorithm W Xie, H Yang AStA Advances in St= atistical Analysis 107 (3), 469-507, 2023	2	2023
One-step sparse estimates in the reverse penalty for high-dimension= al correlated data H Ming, Y Chen, H Yang Journal of Computational and Applied Mathematics 427, = 115119, 2023	= 2	2023
Semiparametric model= averaging for ultrahigh-dimensional conditional quantile predictionCH Guo, J Lv, H Yang, JW Tu, CX Tian Acta Mathematica Sinica, English Series 39 (6), 1171-1202, 2023	2	<= span class=3D"gsc_a_h gsc_a_hc gs_ibl">2023
High-dimensional sparse portfolio = selection with nonnegative constraint S Xia, Y Ya= ng, H Yang Applied Mathematics and Computation = 443, 127766, 2023	8	2023
LS-GNHSVM: a no= vel joint geometrical nonparallel hyperplane support vector machineK Qi, H Yang Expert Systems = with Applications 215, 119413, 2023	2= 023
Multivariate sparse Laplacian shrinkage for joint estimation of two graphi= cal structures Y Yang, S Xia, H Yang Computational Statistics & Data Analysis 178, 107620, 2023	5	2023
Capped asymmetric elastic = net support vector machine for robust binary classification K Qi, H Yang International Journal= of Intelligent Systems 2023 (1), 2201330, 2023	4	2023
A novel robust nonparallel support vector classifier based on= one optimization problem K Qi, H Yang Neural Computing and Applications 35 (1), 799-814, 2023	6	2023
Joint rescaled asymmetric least squ= ared nonparallel support vector machine with a stochastic quasi-Newton base= d algorithm K Qi, H Yang Applied Intelligence 52 (12), 14387-14405, 2022<= /span>	8	2022
Least product relative error estimation for identific= ation in multiplicative additive models H Ming, H= Liu, H Yang Journal of Computational and Appli= ed Mathematics 404, 113886, 2022	20= 22
= Sparse Laplacian shrinkage with the graphical lasso estimator for regressio= n problems S Xia, Y Yang, H Yang Test 31 (1), 255-277, 2022 =	5	2022
Penalized and constrained LAD estimation in fixed and high dimensio= n X Wu, R Liang, H Yang Statistical Papers 63 (1), 53-95, 2022	9	2022
A kind of new time-weighted nonnegative lasso index-tracking model = and its application Q Chen, Q Hu, H Yang, K Qi The North American Journal of Economics and Finan= ce 59, 101603, 2022	18
Joint sparse= principal component regression with robust property K Qi, J Tu, H Yang Expert Systems with Appl= ications 187, 115845, 2022	9=	2022
Nonneg= ative estimation and variable selection via adaptive elastic-net for high-d= imensional data N Li, H Yang, J Yang Communications in Statistics-Simulation and Computation 50 = (12), 4263-4279, 2021	17	2021<= /td>
Model ave= raging marginal regression for high dimensional conditional quantile predic= tion J Tu, H Yang, C Guo, J Lv Statistical Papers 62 (6), 2661-2689, 2021=	2	2021
=E9=87=91=E8=9E=8D=E5=B8=82=E5=9C=BA=E4=B8=8B=E9=9D= =9E=E8=B4=9F=E7=A8=80=E7=96=8F=E7=BB=84 LASSO =E5=9C=A8=E8=82=A1=E6=8C=87= =E8=B7=9F=E8=B8=AA=E4=B8=AD=E7=9A=84=E7=A0=94=E7=A9=B6 =E9=BD=90=E5=87=AF=EF=BC=8C =E6=9D=A8=E8=99=8E =E5=BA=94=E7=94=A8=E6=A6=82=E7=8E=87=E7=BB=9F=E8=AE=A1 37 (3), 221-24= 0, 2021		202= 1
N= onnegative sparse group lasso with an application in financial index tracki= ng QI Kai, Y Hu Chine= se Journal of Applied Probability and Statistics 37 (3), 221-240, 2021	3	2021
Elastic net nonparallel hyperplane = support vector machine and its geometrical rationality K Qi, H Yang IEEE Transactions on Neural = Networks and Learning Systems 33 (12), 7199-7209, 20= 21	38	2021
Conditioning theory of the equality constrained qua= dratic programming and its applications S Wang, H= Yang Linear and Multilinear Algebra 69 (6), 11= 61-1183, 2021	5	2021
Adaptive and reverse= d penalty for analysis of high-dimensional correlated data Y Yang, H Yang Applied Mathematical M= odelling 92, 63-77, 2021	13<= /td>	2021
Nonnega= tive estimation and variable selection under minimax concave penalty for sp= arse high-dimensional linear regression models N = Li, H Yang Statistical Papers 62 (2), 661-680, 2021	20	2021
Bidirectional group rand= om walk based network embedding for asymmetric proximity J Shen, X Shu, H Yang arXiv preprint ar= Xiv:2103.16989, 2021		2021
Rates of convergence of the adaptive elastic net and the post= -selection procedure in ultra-high dimensional sparse models Y Yang, H Yang Communications in S= tatistics-Theory and Methods 50 (1), 73-94, 2021	4	2021
The structured smooth adjustment for square-root regulariza= tion: Theory, algorithm and applications W Xie, H= Yang Knowledge-Based Systems 207, 106278, 2020	4	2020
A robust and efficient estimatio= n and variable selection method for partially linear models with large-dime= nsional covariates H Yang, N Li, J Yang Statistical Papers 61 (5), 1911-1937, 2020	5	2020
Interaction pursuit biconvex optimizationY Yang, S Xia, H Yang arXi= v preprint arXiv:2003.10793, 2020
Weighted composite quantile regression for single index model with missing= covariates at random H Liu, H Yang, C Peng = Computational Statistics 34 (4), 1711-1740, 2019	9	2019
A new adaptive weighted imbalanced = data classifier via improved support vector machines with high-dimension na= ture K Qi, H Yang, Q Hu, D Yang Knowledge-Based Systems 185, 104933, 20= 19	28	2019
Local Walsh-average-based estimation and variable s= election for single-index models J Yang, F Lu, H = Yang Science China Mathematics 62 (10), 1977-19= 96, 2019	6	2019
Rank-based shrinkage est= imation for identification in semiparametric additive models J Yang, H Yang, F Lu Statistical P= apers 60 (4), 1255-1281, 2019	10=	2019=
No= nnegative hierarchical lasso with a mixed -penalty and a fast solver W Xie, H Yang Statisti= cs and Its Interface 12 (4), 599-615, 2019	1	2019
On the penalized maximum likelihood estimation of high-dimensional= approximate factor model S Wang, H Yang, C Yao Computational Statistics 34 (2), 819-846, 2019	5	2019
Performance of the restricted almos= t unbiased type principal components estimators in linear regression model<= /a> Y Li, H Yang Statisti= cal Papers 60 (1), 19-34, 2019	5<= /a>	2019<= /span>
Smo= oth Adjustment for Correlated Effects Y Yang, H Y= ang arXiv preprint arXiv:1901.05229, 2019
Robust variab= le selection of varying coefficient partially nonlinear model based on quan= tile regression J Yang, F Lu, G Tian, X Lu, H Yan= g Statistics and Its Interface 12 (3), 397-413<= span class=3D"gs_oph">, 2019	4	2019
A note on the condition nu= mber of the scaled total least squares problem S = Wang, H Li, H Yang Calcolo 55 (4), 46, 2018	9	2018
Statistical inference on asymptotic= properties of two estimators for the partially linear single-index models<= /a> J Yang, F Lu, H Yang = Statistics 52 (6), 1193-1211, 2018	2	= 2018
WLAD-LASSO method for robust estimation and variable selection in par= tially linear models H Yang, N Li Communications in Statistics-Theory and Methods 47 (20), 4958-= 4976, 2018	6	2018
Semiparametric model= averaging for high dimensional conditional quantile prediction J Tu, H Yang, C Guo arXiv prepri= nt arXiv:1809.01364, 2018		2018
Two step estimations for a single-index varying-coeffici= ent model with longitudinal data C Guo, H Yang, J= Lv Statistical Papers 59 (3), 957-983, 2018	8	2018
=E7=BA=B5=E5=90=91=E6=95=B0=E6=8D= =AE=E7=9A=84=E6=9C=89=E6=95=88=E7=A7=A9=E6=8E=A8=E6=96=AD=E5=9F=BA=E4=BA=8E= =E4=BF=AE=E6=AD=A3=E7=9A=84 Cholesky =E5=88=86=E8=A7=A3 =E5=90=95=E6=99=B6=EF=BC=8C =E9=83=AD=E6=9C=9D=E4=BC=9A=EF=BC=8C =E6= =9D=A8=E8=99=8E=EF=BC=8C =E6=9D=8E=E5=A9=B7=E5=A9=B7 =E6=95=B0=E5=AD=A6=E5=AD=A6=E6=8A=A5 61 (4), 549-568, 2018	1	2018
2	2018
Quantile regression for robust inferen= ce on varying coefficient partially nonlinear models J Yang, F Lu, H Yang Journal of the Korean = Statistical Society 47 (2), 172-184, 2018	9	2018
Robust variable selection in high-dimensional varying coefficient = models based on weighted composite quantile regression C Guo, H Yang, J Lv Statistical Papers 58= (4), 1009-1033, 2017	18	2017
Quantile = regression for robust estimation and variable selection in partially linear= varying-coefficient models J Yang, F Lu, H Yang<= /div> Statistics 51 (6), 1179-1199, 2017	12	2017
2= 017
Variable selection in partially linear additive models for modal regressio= n J Lv, H Yang, C Guo Communications in Statistics-Simulation and Computation 46 (7), 5646-5665<= span class=3D"gs_oph">, 2017	20	2017
A moving average Cholesk= y factor model in covariance modeling for composite quantile regression wit= h longitudinal data J Lv, C Guo, H Yang, Y Li Computational Statistics & Data Analysis 112, = 129-144, 2017	14	2017
Penalized composite= quantile estimation for censored regression model with a diverging number = of parameters H Liu, H Yang Communications in Statistics-Theory and Methods 46 (13), 6558-6578, 2017	<= /a>	2017<= /span>
Qua= ntile regression and variable selection for single-index varying-coefficien= t models J Yang, H Yang Communications in Statistics-Simulation and Computation 46 (6), 4637-465= 3, 2017	9	2017
Robust modal estimation an= d variable selection for single-index varying-coefficient models J Yang, H Yang Communications i= n Statistics-Simulation and Computation 46 (4), 2976-2997, 2017	4	2017
6	2017
Robust estimation and variable selection in censored partiall= y linear additive models H Liu, H Yang, X Xia Journal of the Korean Statistical Society 46 (1), = 88-103, 2017	= 16	2017
A note on a discrete= time MAP risk model C Liu, Z Zhang, H Yang = Journal of Computational and Applied Mathematics 309= , 111-121, 2017	6	2017
More on the two-pa= rameter estimation in the restricted regression Y= Li, H Yang Communications in Statistics-Theory= and Methods 45 (24), 7184-7196, 2016	5	2016
Joint estimation for single index mean=E2=80=93covariance models wi= th longitudinal data C Guo, H Yang, J Lv, J Wu Journal of the Korean Statistical Society 45 (4),= 526-543, 2016	4	2016
On the condition n= umber theory of the equality constrained indefinite least squares problem S Wang, H Li, H Yang a= rXiv preprint arXiv:1611.05949, 2016	1	2016
On the condition number of equality constrained indefinite least squa= res problem S Wang, H Yang arXiv preprint arXiv:1611.05949, 2016	2	2016
= Feature screening for generalized varying coefficient models wit= h application to dichotomous responses X Xia, H Y= ang, J Li Computational Statistics & Data A= nalysis 102, 85-97, 2016	17=	2016
Robust= variable selection and parametric component identification in varying coef= ficient models H Yang, J Lv, C Guo Communications in Statistics-Theory and Methods 45 (18), 5533= -5549, 2016	<= a href=3D"https://scholar.google.com/scholar?oi=3Dbibs&hl=3Dzh-CN&c= ites=3D12127072056157398032" class=3D"gsc_a_ac gs_ibl">4	2016
Restricted estimatio= n and testing of hypothesis in linear measurement errors models W Li, T Li, H Yang Communication= s in Statistics-Theory and Methods 45 (18), 5318-5330, 2016	1	2016
Robust estimation for varying index coefficient = models J Lv, H Yang, C Guo Computational Statistics 31 (3), 1131-1167, 20= 16	4	2016
Smoothing combined generalized estimating equations i= n quantile partially linear additive models with longitudinal data J Lv, H Yang, C Guo Computati= onal Statistics 31 (3), 1203-1234, 2016 =	8	2016
Smooth-threshold estimating equations for varying coefficient parti= ally nonlinear models based on orthogonality-projection method J Yang, H Yang Journal of Computa= tional and Applied Mathematics 302, 24-37, 2016	23	2016
Penalized LAD regression for single-index models H Yang, J Lv, C Guo Communicati= ons in Statistics-Simulation and Computation 45 (7), 2392-2408, 2016	4	2016
Assessing local influence for ellip= tical linear models under equality constraints H = Yang, L Yang Communications in Statistics-Theor= y and Methods 45 (15), 4517-4527, 2016 <= /td>	1	2016
Robust estimation and variable selection for varying-coefficient si= ngle-index models based on modal regression H Yan= g, J Lv, C Guo Communications in Statistics-The= ory and Methods 45 (14), 4048-4067, 2016	10	2016
Generalized varying index coefficient models C Guo, H Yang, J Lv Journal of Computation= al and Applied Mathematics 300, 1-17, 2016	7	2016
On a nonparametric estimator for the finite time survival probabil= ity with zero initial surplus Z Zhang, H Yang, H = Yang Acta Mathematicae Applicatae Sinica, Engli= sh Series 32 (3), 739-754, 2016	2016=
Po= sitive-rule stein-type almost unbiased ridge estimator in linear regression= model C Liu, H Yang = Communications in Statistics-Theory and Methods 45 (8), 2228-2255, 2016	3	2016
Regularized estimation for the leas= t absolute relative error models with a diverging number of covariates<= div class=3D"gs_gray">X Xia, Z Liu, H Yang Comp= utational Statistics & Data Analysis 96, 104-119= , 2016	26	2016
Penalized inverse probability weighted estimators = for weighted rank regression with missing covariates H Yang, C Guo, J Lv Communications in Stati= stics-Theory and Methods 45 (5), 1388-1402, 2016		2016
Variable selection for partially ti= me-varying coefficient error-in-variables models = X Xia, H Yang Statistics 50 (2), 278-297, 2016	5	<= span class=3D"gsc_a_h gsc_a_hc gs_ibl">2016
Inverse probability weighted estim= ators for single-index models with missing covariates T Li, H Yang Communications in Statistics-= theory and Methods 45 (5), 1199-1214, 2016	9	2016
A robust penalized estimation for identification in semiparametric= additive models J Yang, H Yang Statistics & Probability Letters 110, 268-277, 2016	6	2016
Variable selection for generalized var= ying coefficient models with longitudinal data H = Yang, C Guo, J Lv Statistical Papers 57 (1), 11= 5-132, 2016	<= a href=3D"https://scholar.google.com/scholar?oi=3Dbibs&hl=3Dzh-CN&c= ites=3D7413729549540164661" class=3D"gsc_a_ac gs_ibl">14	2016
More on the unbiased= ridge regression estimation J Wu, H Yang Statistical Papers 57 (1), 31-42, 2016	5	2016
Penalized weighted composite quantile estimators = with missing covariates H Yang, H Liu Statistical Papers 57 (1), 69-88, 2= 016	29	2016
Further research on the principal component two-p= arameter estimator in linear model H Yang, J Huan= g Communications in Statistics-Theory and Metho= ds 45 (3), 566-576, 2016	6<= /td>	2016
A fle= xible condition number for weighted linear least squares problem and its st= atistical estimation H Yang, S Wang Journal of Computational and Applied Mathematics 292, 320-32= 8, 2016	8	2016
=E6=A6=82=E7=8E=87=E8=AE= =BA=E5=9F=BA=E7=A1=80 =E5=BE=90=E5=BB=BA=E6=96=87= =E6=9D=A8=E8=99=8E=E3=80=81		2016
=E9=87=91=E8=9E=8D=E5=A4=A7=E6=95=B0=E6=8D=AE=E7=BB= =9F=E8=AE=A1=E6=96=B9=E6=B3=95=E4=B8=8E=E5=AE=9E=E8=AF=81 =E6=9D=A8=E8=99=8E =E7=A7=91=E5=AD=A6= =E5=87=BA=E7=89=88=E7=A4=BE, 2016	20= 16
Variable selection for semiparametric varying coefficient partially linea= r errors-in-Variables (EV) model with missing response H Yang, X Xia Communications in Statistic= s-Theory and Methods 44 (21), 4521-4539, 2015=	5	2015
Robust variable selection in modal varying-coefficient models= with longitudinal H Yang, J Lv, C Guo Journal of Statistical Computation and Simulation 85 (15)= , 3064-3079, 2015	6	2015
Condition num= bers for the nonlinear matrix equation and their statistical estimation= S Wang, H Yang, H Li Lin= ear Algebra and its Applications 482, 221-240, 2015<= /span>	21	2015
On the principal component Liu-type estimator in linear= regression J Wu, H Yang Communications in Statistics-Simulation and Computation 44 (8), 2061-20= 72, 2015	14	2015
A ruin model with com= pound poisson income and dependence between claim sizes and claim intervals= Y Hao, H Yang Acta M= athematicae Applicatae Sinica, English Series 31 (2), 445-452, 2015	5	2015
Robust smooth-threshold estimating equa= tions for generalized varying-coefficient partially linear models based on = exponential score function J Lv, H Yang, C Guo Journal of Computational and Applied Mathematics = 280, 125-140, 2015	5	2015
Weighted comp= osite quantile regression estimation and variable selection for varying coe= fficient models with heteroscedasticity H Yang, J= Lv, C Guo Journal of the Korean Statistical So= ciety 44 (1), 77-94, 2015	16<= /td>	2015
=E5= =9B=9E=E5=BD=92=E7=B3=BB=E6=95=B0=E7=9A=84=E5=8A=A0=E6=9D=83=E6=B7=B7=E5=90= =88=E4=BC=B0=E8=AE=A1=E4=B8=8E=E6=9C=80=E5=B0=8F=E4=BA=8C=E4=B9=98=E4=BC=B0= =E8=AE=A1=E7=9A=84=E7=9B=B8=E5=AF=B9=E6=95=88=E7=8E=87 =E9=82=AC=E5=90=89=E6=B3=A2=EF=BC=8C =E6=9D=A8=E8=99=8E =E5=BA=94=E7=94=A8=E6=A6=82=E7=8E=87=E7=BB=9F=E8=AE=A1 31 (1= ), 11-19, 2015	4	2015
Eigenvalue Condi= tion and model selection consistency of lasso Y Y= ang, H Yang arXiv preprint arXiv:1502.01798, 2015	=	2015
On a= principal component two-parameter estimator in linear model with autocorre= lated errors J Huang, H Yang Statistical Papers 56 (1), 217-230, 2015	5	2015
An efficient and robust variable selection method for long= itudinal generalized linear models J Lv, H Yang, = C Guo Computational Statistics & Data Analy= sis 82, 74-88, 2015	35
Perturbatio= n analysis for the symplectic QR factorization H = Li, H Yang, H Shao Linear and Multilinear Algeb= ra 63 (1), 78-96, 2015	3
Improvemen= ts in the upper bounds for the spread of a matrix P Zhang, H Yang A, A 1, 2, 2015	4	2015
SCAD penalized rank regression with a divergin= g number of parameters H Yang, C Guo, J Lv <= div class=3D"gs_gray">Journal of Multivariate Analysis 133, 321-333, 2015	10	<= span class=3D"gsc_a_h gsc_a_hc gs_ibl">2015
Efficiency of a stochastic restr= icted two-parameter estimator in linear regression Y Li, H Yang Applied Mathematics and Computat= ion 249, 371-381, 2014	15	2014=
On a pe= rturbed Sparre Andersen risk model with dividend barrier and dependence= Z Zhang, X Wu, H Yang Jo= urnal of the Korean Statistical Society 43 (4), 585-598, 2014	5	2014
12	2014
A furth= er study of predictions in linear mixed models H = Yang, H Ye, K Xue Communications in Statistics-= Theory and Methods 43 (20), 4241-4252, 2014	33	2014
= Robust variable selection in semiparametric mean-covariance r= egression for longitudinal data analysis C Guo, H= Yang, J Lv Applied Mathematics and Computation= 245, 343-356, 2014	5	2014
Adaptive elas= tic net and Separate Selection from Least Squares for ultra-high dimensiona= l regression models Y Yang, H Yang arXiv preprint arXiv:1410.3566, 2014	2	2014
Local influence analysis for the ridge regression un= der stochastic linear restrictions L Yang, H Yang= Communications in Statistics-Theory and Method= s 43 (17), 3580-3595, 2014	2=	2014
Prel= iminary Test Estimators Induced by Three Large Sample Tests for Stochastic = Constraints in a Regression Model with Multivariate Student-t ErrorX Chang, H Yang Communicatio= ns in Statistics-Theory and Methods 43 (17), 3629-3640, 2014	2	2014
Some representations for the Drazin inverse o= f a modified matrix A Shakoor, H Yang, I Ali Calcolo 51 (3), 505-514, 201= 4	10	2014
A robust and efficient estimation and variable selec= tion method for partially linear single-index models H Yang, J Yang Journal of Multivariate Anal= ysis 129, 227-242, 2014	24=	2014
The = adaptive L1-penalized LAD regression for partially linear single-index mode= ls H Yang, J Yang Jou= rnal of Statistical Planning and Inference 151, 73-89, 2014	10	2014
A note on multiplicative perturbation bounds f= or the Moore=E2=80=93Penrose inverse H Yang, P Zh= ang Linear and Multilinear Algebra 62 (6), 831-= 838, 2014	11	2014
On a nonparametric es= timator for ruin probability in the classical risk model Z Zhang, H Yang, H Yang Scandinavian Ac= tuarial Journal 2014 (4), 309-338, 2014 =	36	2014
On mixed and componentwise condition numbers for indefinite least= squares problem H Li, S Wang, H Yang Linear Algebra and its Applications 448, 104-129, 2014	17	2014
Equivalence of two tests in varyi= ng coefficient partially linear errors in variable model with missing respo= nses H Yang, X Xia Jo= urnal of the Korean Statistical Society 43 (1), 79-90, 2014	7	2014
Refinements of the Heron and Heinz means inequ= alities for matrices I Ali, H Yang, A Shakoor J. math. inequal 8 (1), 107-112, 2014	5	2014
4	2014
A two-parameter = estimator in the negative binomial regression model J Huang, H Yang Journal of Statistical Compu= tation and Simulation 84 (1), 124-134, 2014	63	2014
Two classes of almost unbiased type principal component estim= ators in linear regression model Y Li, H Yang Journal of Applied Mathematics 2014 (1), 639070, 2014	3	2014
On the stochastic restri= cted almost unbiased estimators in linear regression model J Wu, H Yang Communications in Statis= tics-Simulation and Computation 43 (2), 428-440, 201= 4	10	2014
Two stochastic restricted principal components regre= ssion estimator in linear regression J Wu, H Yang= Communications in Statistics-Theory and Method= s 42 (20), 3793-3804, 2013	8=	2013
Ruin= probability in a semi-markov risk model with constant interest force and h= eavy-tailed claims Y Hu, XUE Kai Acta Mathematica Scientia 33 (4), 998-1006, 2013	2	2013
39	2013
On a Sparre Andersen risk model perturb= ed by a spectrally negative L=C3=A9vy process Z Z= hang, H Yang, H Yang Scandinavian Actuarial Jou= rnal 2013 (3), 213-239, 2013	6	2013
New= perturbation bounds for the subunitary polar factors P Zhang, H Yang, H Li Linear and Multiline= ar Algebra 61 (4), 517-526, 2013	201= 3
New perturbation bounds for nonnegative and positive polar factorsHY Li, H Yang, H Shao Math. = Inequal. Appl 16, 349-362, 2013	2013=
= Weighted stochastic restricted estimation in linear measurement error model= s W Li, H Yang Commun= ications in Statistics-Simulation and Computation 42 (4), 932-968, 2013	8	2013
Performances of the positive-rule= stein-type ridge estimator in a linear regression model with spherically s= ymmetric error distributions J Xu, H Yang Communications in Statistics-Theory and Methods 42 (3)= , 543-560, 2013	2	2013
Two-sided gener= alized hyperbolic QR factorization and its perturbation analysis H Li, H Yang, H Shao Linear Alg= ebra and its Applications 438 (3), 1267-1292, 2013	2	2013
=E7=BB=9F=E8=AE=A1=E7=9F=A5=E7=8E=84=E5=A6=99 =E6=9D=A8=E8=99=8E	2013
Some results for the Drazin inverses of the sum = of two matrices and some block matrices A Shakoor= , H Yang, I Ali Journal of Applied Mathematics = 2013 (1), 804313, 2013	2	2013<= /td>
Some Re= sults on the Problem of Updating the Hyperbolic Matrix FactorizationsH LI, Y Hu =E6=95=B0=E5=AD= =A6=E7=A0=94=E7=A9=B6=E5=8F=8A=E5=BA=94=E7=94=A8, 20= 13		2013
On the weighted mixed almo= st unbiased ridge estimator in stochastic restricted linear regression<= div class=3D"gs_gray">C Liu, H Yang, J Wu Journ= al of Applied Mathematics 2013 (1), 902715, 2013	12	2013
The Drazin inverses of the sum of two matrices and block = matrix A Shakoor, H Yang, I Ali Journal of applied mathematics & informatics 31 (3-4), 343= -352, 2013	9	2013
=E5=9F=BA=E4=BA=8E= W, LR =E5=92=8C LM =E6=A3=80=E9=AA=8C=E7=9A=84=E9=A2=84=E6=A3=80=E9=AA=8C= =E4=B8=A4=E5=8F=82=E6=95=B0=E4=BC=B0=E8=AE=A1 =E5= =B8=B8=E6=96=B0=E9=94=8B=EF=BC=8C =E6=9D=A8=E8=99=8E =E5=BA=94=E7=94=A8=E6=A6=82=E7=8E=87=E7=BB=9F=E8=AE=A1 28 (6), 572-58= 2, 2012		201= 2
A stochastic restricted k=E2=80=93d class estimator H Yang, J Wu Statistics 46 (6), 75= 9-766, 2012	<= a href=3D"https://scholar.google.com/scholar?oi=3Dbibs&hl=3Dzh-CN&c= ites=3D710908495998837774" class=3D"gsc_a_ac gs_ibl">11	2012
On a Sparre Anders= en risk model with time-dependent claim sizes and jump-diffusion perturbati= on Z Zhang, H Yang, H Yang Methodology and computing in applied probability 14 (4), 973-995, 2012	32	2012
Matrix Euclidean norm Wieland= t inequalities and their applications to statistics L Wang, H Yang Statistical Papers 53 (3), 52= 1-530, 2012	<= a href=3D"https://scholar.google.com/scholar?oi=3Dbibs&hl=3Dzh-CN&c= ites=3D12137994492609906442" class=3D"gsc_a_ac gs_ibl">8	2012
Combining two-para= meter and principal component regression estimators X Chang, H Yang Statistical Papers 53 (3), 5= 49-562, 2012	= 52	2012
Performance of the= preliminary test two-parameter estimators based on the conflicting test st= atistics in a regression model with Student's t error X Chang, H Yang Statistics 46 (3),= 291-303, 2012	10	2012
Perturbation an= alysis for the hyperbolic QR factorization H Li, = H Yang, H Shao Computers & Mathematics with= Applications 63 (12), 1607-1620, 2012 <= /td>	8	2012
Perturbation analysis for block downdating of the generalized Cho= lesky factorization H Li, H Yang, H Shao Applied Mathematics and Computation 218 (18), 9451-9461= , 2012	4	2012
A new Liu-type estimator= in linear regression model Y Li, H Yang Statistical Papers 53 (2), 427-437, 2012	86	2012
31<= /a>	2012<= /span>
S= ome comments on: Ozkale, MR, Kaciranlar, K.(2008): Comparisons of the rk cl= ass estimator to the ordinary least squares estimator under the Pitman's cl= oseness criterion =E2=80=A6 W Li, H Yang, J = Wu Statistical Papers 53 (2), 497, 2012	4	2012
=E5=8C=85=E5=90=AB=E5=A4=9A=E4=BD=99=E5= =9B=9E=E5=BD=92=E5=8F=98=E9=87=8F=E7=9A=84=E9=94=99=E8=AF=AF=E6=8C=87=E5=AE= =9A=E6=A8=A1=E5=9E=8B=E7=9A=84=E9=9A=8F=E6=9C=BA=E7=BA=A6=E6=9D=9F Liu =E4= =BC=B0=E8=AE=A1 =E5=BE=90=E5=BB=BA=E6=96=87=EF=BC= =8C =E6=9D=A8=E8=99=8E =E5=BA=94=E7=94=A8=E6=A6= =82=E7=8E=87=E7=BB=9F=E8=AE=A1 28 (2), 123-133, 2012=		2012
=E9=87=91=E8=9E=8D=E6=97=B6= =E5=BA=8F=E4=B8=AD=E5=BC=82=E5=B8=B8=E6=95=B0=E6=8D=AE=E6=8C=96=E6=8E=98=E7= =AE=97=E6=B3=95=E8=AE=BE=E8=AE=A1=E5=8F=8A=E5=AE=9E=E8=AF=81=E5=88=86=E6=9E= =90 =E6=9D=A8=E8=99=8E=EF=BC=8C =E6=9D=8E=E5=BC= =BA =E4=B8=AD=E5=9B=BD=E7=AE=A1=E7=90=86=E7=A7= =91=E5=AD=A6, 7-11, 2012	10=	2012
On t= he preliminary test backfitting and Speckman estimators in partially linear= models and numerical comparisons J Xu, H Yang Communications in Statistics-Simulation and Compu= tation 41 (3), 327-341, 2012	2	2012
On= the Stein-type Liu estimator and positive-rule Stein-type Liu estimator in= multiple linear regression models J Xu, H Yang Communications in Statistics-Theory and Methods = 41 (5), 791-808, 2012	10	2012
Erratum= to =E2=80=9CPerturbation analysis for the hyperbolic QR factorization=E2= =80=9D[Comput. Math. Appl. 63 (2012) 1607=E2=80=931620] H Li, H Yang, H Shao Computers and Mathe= matics with Applications 64 (4), 686, 2012		2012
Relative and Absolute Perturbation Bo= unds for Weighted Polar Decomposition P Zhang, H = Yang, H Li Journal of Applied Mathematics 2012 = (1), 219025, 2012	1	2012
Preliminary te= st almost unbiased ridge estimator in a linear regression model with multiv= ariate Student-t errors J Xu, H Yang Acta et Commentationes Universitatis Tartuensis de Mathemat= ica 15 (1), 27-43, 2011	2	2011=
Estimat= ion in singular linear models with stochastic linear restrictions and linea= r equality restrictions H Yang, J Wu Communications in Statistics-Theory and Methods 40 (24), 43= 64-4371, 2011	6	2011
Correction on est= imation for a partial-linear single-index model T= T Li, H Yang, JL Wang, LG Xue, LX Zhu The Annal= s of Statistics 39 (6), 3441-3443, 2011 =	6	2011
Estimation for a partial-linear single-index model TT Li, H Yang, JL Wang, LG Xue, LX Zhu	2	2011
Some matrix norm Kantorovich inequalities and their = applications H Yang, J Wu Communications in Statistics-Theory and Methods 40 (22), 4078-4085, 2011	6	2011
More on the bias and varia= nce comparisons of the restricted almost unbiased estimators J Xu, H Yang Communications in Sta= tistics-Theory and Methods 40 (22), 4053-4064, 2011<= /span>	38	2011
Some results on the partial orderings of block matri= ces X Liu, H Yang Jou= rnal of Inequalities and Applications 2011 (1), 54, = 2011	4	2011
=E4=B8=80=E8=88=AC=E9=A3=8E=E9=99=A9=E6=A8=A1=E5= =9E=8B=E7=9A=84=E7=BB=9D=E5=AF=B9=E7=A0=B4=E4=BA=A7=E6=97=B6=E9=97=B4=E6=9D=A8=E8=99=8E=EF=BC=8C =E9=BB=84=E9=9B=AF=E5=A9= =B7 =E5=BA=94=E7=94=A8=E6=A6=82=E7=8E=87=E7=BB= =9F=E8=AE=A1 27 (4), 380-390, 2011		2011
Mixed-type reverse-order laws of (AB)(1, 2, 3) = and (AB)(1, 2, 4) H Yang, X Liu Applied mathematics and computation 217 (24), 10361-10367, 2011	6	2011
On a compound Poisson risk mo= del with delayed claims and random incomes Y Hao,= H Yang Applied mathematics and computation 217= (24), 10195-10204, 2011	11=	2011
On t= he performance of the jackknifed modified ridge estimator in the linear reg= ression model with correlated or heteroscedastic errors Y Li, H Yang Communications in Statistic= s-Theory and Methods 40 (15), 2695-2708, 2011=	13	2011
Two kinds of restricted modified estimators in linear regre= ssion model Y Li, H Yang Journal of Applied Statistics 38 (7), 1447-1454,= 2011	13	2011
On the restricted r=E2=80=93k cla= ss estimator and the restricted r=E2=80=93d class estimator i= n linear regression J Xu, H Yang Journal of Statistical Computation and Simulation 81 (6), 679-= 691, 2011	27	2011
Semiparametric EGARCH= model with the case study of China stock market = H Yang, X Wu Economic Modelling 28 (3), 761-766= , 2011	6	2011
Preliminary test Liu es= timators based on the conflicting W, LR and LM tests in a regression model = with multivariate Student-t error H Yang, = J Xu Metrika 73 (3), 275-292, 2011	17	2011
25	2011
More on the Preliminary Test Estimator in Almost Unb= iased Liu Regression Y Li, H Yang, J Xu Communications in Statistics-Theory and Methods 40 (13),= 2292-2304, 2011	2	2011
A stochastic res= tricted two-parameter estimator in linear regression model H Yang, J Cui Communications in stati= stics-theory and methods 40 (13), 2318-2325, 2011	14	2011
A new ridge-type estimator in stochastic restricted lin= ear regression Y Li, H Yang Statistics 45 (2), 123-130, 2011 =	33	2011
The compound Poisson risk model with dependence under a multi-lay= er dividend strategy Z Zhang, H Yang Applied Mathematics-A Journal of Chinese Universities 26 (1= ), 1-13, 2011	13	2011
On the absolute = ruin in a MAP risk model with debit interest Z Zh= ang, H Yang, H Yang Advances in Applied Probabi= lity 43 (1), 77-96, 2011	19<= /td>	2011
On th= e restricted almost unbiased estimators in linear regression J Xu, H Yang Journal of Applied St= atistics 38 (3), 605-617, 2011	22=	2011=
= =E6=95=B0=E5=AD=A6=E6=8A=BD=E8=B1=A1=E4=B8=8E=E8=89=BA=E6=9C=AF =E6=9D=A8=E8=99=8E =E5=A4=A7=E5= =AD=A6=E7=A7=91=E6=99=AE 5 (1), 29-30, 2011		2011
=E5=8F=97=E7=BA=A6=E6=9D=9F=E5=9B=9E= =E5=BD=92=E6=A8=A1=E5=9E=8B=E5=8F=82=E6=95=B0=E7=9A=84=E7=BB=9F=E4=B8=80=E5= =87=A0=E4=B9=8E=E6=97=A0=E5=81=8F=E4=BC=B0=E8=AE=A1 =E9=BB=8E=E9=9B=85=E8=8E=B2=EF=BC=8C =E6=9D=A8=E8=99=8E =E7=B3=BB=E7=BB=9F=E7=A7=91=E5=AD=A6=E4=B8=8E=E6=95=B0=E5=AD= =A6 31 (1), 105-113, 2011	3<= /td>	2011
A not= e on the reverse order laws for {1, 2, 3}-and {1, 2, 4}-inverses of multipl= e matrix products X Liu, H Yang The Electronic Journal of Linear Algebra 22, 620-629, 2011	4	2011
Gerber=E2=80=93Shiu analysis in a= perturbed risk model with dependence between claim sizes and interclaim ti= mes Z Zhang, H Yang J= ournal of Computational and Applied Mathematics 235 (5), 1189-1204, 2011	52	<= span class=3D"gsc_a_h gsc_a_hc gs_ibl">2011
The Drazin inverse of the sum of= two matrices and its applications H Yang, X Liu<= /div> Journal of computational and applied mathematic= s 235 (5), 1412-1417, 2011	103	2011
A = ruin model with random income and dependence between claim sizes and claim = intervals H Yang, Y Hao Acta Mathematicae Applicatae Sinica, English Series 26 (4), 625-632, 2010	4	= 2010
Comparison of two estimators of= parameters under Pitman nearness criterion H Yan= g, W Li, J Xu Communications in Statistics=E2= =80=94Theory and Methods 39 (17), 3081-3094, 2010	5	2010
Two Kinds of Restricted Estimators in Singular Linear Reg= ression Y Li, H Yang = Communications in Statistics=E2=80=94Theory and Methods 39 (14), 2594-2603<= span class=3D"gs_oph">, 2010	1	2010
Multiplicative perturba= tion bounds for weighted unitary polar factor H Y= ang, H Li, H Shao Math. Inequal. Appl 3, 537-55= 4, 2010	1	2010
When does surplus reach = a given target before ruin in the Markov-modulated diffusion model?H Yang, Z Zhang Journal of t= he Korean Statistical Society 39 (2), 207-219, 2010<= /span>	2	2010
On a discrete risk model with two-sided jumpsH Yang, Z Zhang Journal of = computational and applied mathematics 234 (3), 835-844, 2010	6	2010
A new stochastic mixed ridge estimator in linea= r regression model Y Li, H Yang Statistical Papers 51 (2), 315-323, 201= 0	74	2010
Further Results on the Reverse Order Law for-Inverse= and-Inverse of a Matrix Product D Liu, H Yang Journal of Inequalities and Applications 2010 (1)= , 312767, 2010	11	2010
On a risk model = with stochastic premiums income and dependence between income and loss<= div class=3D"gs_gray">Z Zhang, H Yang Journal o= f computational and applied mathematics 234 (1), 44-57, 2010	30	2010
Lavoie inequalities for weighted generalized = inverses of matrices H Yang, H Li, H Shao Linear and Multilinear Algebra 58 (3), 285-295, 2010		2010<= /td>
A gener= alized penalty function in the Sparre=E2=80=93Andersen risk model with two-= sided jumps Z Zhang, H Yang Statistics & probability letters 80 (7-8), 597-607, 2010	18	2010
A new two-parameter estimator in linea= r regression H Yang, X Chang Communications in Statistics=E2=80=94Theory and Methods 39 (6), 923= -934, 2010	155	2010
The perturbed comp= ound Poisson risk model with two-sided jumps Z Zh= ang, H Yang, S Li Journal of Computational and = Applied Mathematics 233 (8), 1773-1784, 2010<= /div>	71	2010
The reverse order law for {1, 3, 4}-inverse of the product of= two matrices D Liu, H Yang Applied mathematics and computation 215 (12), 4293-4303, 2010	23	2010
A note on the perturbation analys= is for the generalized Cholesky factorization H L= i, H Yang, H Shao Applied mathematics and compu= tation 215 (11), 4022-4027, 2010	201= 0
Empirical likelihood for semiparametric varying coefficient partially line= ar models with longitudinal data H Yang, T Li Statistics & probability letters 80 (2), 111-1= 21, 2010	17	2010
=E9=87=8D=E6=9E=84=E6= =95=B0=E5=AD=A6=E5=AE=9E=E9=AA=8C=E4=BD=93=E7=B3=BB=E4=B8=8E=E5=A4=A7=E5=AD= =A6=E7=94=9F=E6=95=B0=E5=AD=A6=E5=88=9B=E6=96=B0=E8=83=BD=E5=8A=9B=E5=9F=B9= =E5=85=BB =E6=9B=B9=E8=93=93=EF=BC=8C =E6=9D=A8= =E8=99=8E =E4=B8=AD=E5=9B=BD=E5=A4=A7=E5=AD=A6= =E6=95=99=E5=AD=A6, 68-69, 2010	2010=
= Relative perturbation bounds for weighted polar decomposition H Li, H Yang Computers & mathe= matics with applications 59 (2), 853-860, 2010	2	2010
On a class of renewal risk model with random income H Yang, Z Zhang Applied Stoch= astic Models in Business and Industry 25 (6), 678-695, 2009	29	2009
On a perturbed Sparre Andersen risk model with= multi-layer dividend strategy H Yang, Z Zhang Journal of computational and applied mathematics = 232 (2), 612-624, 2009	6	2009
Several= matrix Euclidean norm inequalities involving Kantorovich inequalityL Wang, H Yang Journal of I= nequalities and Applications 2009 (1), 291984, 2009<= /span>	6	2009
The Gerber=E2=80=93Shiu discounted penalty functions= for a risk model with two classes of claims Z Zh= ang, S Li, H Yang Journal of Computational and = Applied Mathematics 230 (2), 643-655, 2009	37	2009
= A note on an unusual type of generalized polar decomposition<= /a> H Li, H Yang Linear a= lgebra and its applications 431 (5-7), 518-526, 2009=		2009
An alternative form of the Wa= tson efficiency H Yang, L Wang Journal of statistical planning and inference 139 (8), 2767-2774<= span class=3D"gs_oph">, 2009	15	2009
Mean squared error mat= rix comparisons of some restricted almost unbiased estimators H Yang, Y Li, J Xu Communications = in Statistics-Theory and Methods 38 (14), 2321-2332,= 2009	2	2009
Quasi-minimax estimation in the general linear re= gression model H Yang, L Wang, L Song Journal of statistical planning and inference 139 (7), 211= 7-2125, 2009	= 4	2009
An alternative sto= chastic restricted Liu estimator in linear regression H Yang, J Xu Statistical Papers 50 (3), 63= 9-647, 2009	<= a href=3D"https://scholar.google.com/scholar?oi=3Dbibs&hl=3Dzh-CN&c= ites=3D14230936223689656614" class=3D"gsc_a_ac gs_ibl">61	2009
The representation= of generalized inverse AT, S (2, 3) and its applications H Yang, D Liu Journal of Computational= and Applied Mathematics 224 (1), 204-209, 2009	21	2009
Weighted polar decomposition H Y= ang, HY Li J. Math. Res. Exposition 29 (5), 787= -798, 2009	13	2009
=E6=9C=AC=E7=A7=91= =E7=BB=9F=E8=AE=A1=E5=AD=A6=E4=B8=93=E4=B8=9A (=E9=87=91=E8=9E=8D=E4=B8=8E= =E7=B2=BE=E7=AE=97=E6=96=B9=E5=90=91) =E7=9A=84=E7=89=B9=E8=89=B2=E4=B8=8E= =E5=8F=91=E5=B1=95=E6=80=9D=E8=B7=AF=E2=80=94=E2=80=94=E4=BB=A5=E9=87=8D=E5= =BA=86=E5=A4=A7=E5=AD=A6=E6=95=B0=E7=90=86=E5=AD=A6=E9=99=A2=E4=B8=BA=E4=BE= =8B =E6=AE=B5=E6=AD=A3=E6=95=8F=EF=BC=8C =E6=9D= =A8=E8=99=8E =E6=95=99=E8=82=B2=E4=B8=8E=E6=95= =99=E5=AD=A6=E7=A0=94=E7=A9=B6 5, 83-85, 2009=	1	2009
On Weighted Partial Orderings on the Set of Rectangular Comp= lex Matrices H Li, H Yang, H Shao JIPAM. Journal of Inequalities in Pure & Applied Mathemati= cs [electronic =E2=80=A6, 2009		2009
26<= /td>	2009
Ruin = problems in a discrete Markov risk model H Yang, = Z Zhang, C Lan Statistics & Probability Let= ters 79 (1), 21-28, 2009	28<= /td>	2009
The p= erturbed compound Poisson risk model with multi-layer dividend strategy= H Yang, Z Zhang Statisti= cs & Probability Letters 79 (1), 70-78, 2009	26	2009
=E7=BA=BF=E6=80=A7=E5=9B=9E=E5=BD=92=E8=AF=8A=E6=96=AD=E4= =B8=AD=E7=9A=84=E9=AB=98=E6=9D=A0=E6=9D=86=E7=82=B9=E5=BA=A6=E9=87=8F=E6=9D=A8=E8=99=8E=EF=BC=8C =E9=82=B5=E5=8D=8E=E5=B7=A5=E7=A8=8B=E6=95=B0=E5=AD=A6=E5=AD=A6=E6=8A=A5= 26 (1), 123-132, 2009	6	2009
=E5=B9= =BF=E4=B9=89=E9=9D=9E=E8=B4=9F=E4=B8=8E=E6=AD=A3=E6=9E=81=E5=9B=A0=E5=AD=90= =E7=9A=84=E4=B9=98=E6=B3=95=E6=89=B0=E5=8A=A8=E7=95=8C =E6=9D=8E=E5=AF=92=E5=AE=87=EF=BC=8C =E6=9D=A8=E8=99=8E=EF=BC=8C =E9= =82=B5=E5=8D=8E =E5=BA=94=E7=94=A8=E6=95=B0=E5= =AD=A6=E5=AD=A6=E6=8A=A5, 912-922, 2009 =	1	2009
=E5=8A=A0=E6=9D=83=E6=9E=81=E5=88=86=E8=A7=A3 (=E8=8B=B1=E6=96=87= ) =E6=9D=A8=E8=99=8E=EF=BC=8C =E6=9D=8E=E5=AF=92= =E5=AE=87 =E6=95=B0=E5=AD=A6=E7=A0=94=E7=A9=B6= =E4=B8=8E=E8=AF=84=E8=AE=BA 5, 2009	1	= 2009
Perturbation bounds for weighted polar decomposition in the weighted = unitarily invariant norm H Yang, H Li Numerical Linear Algebra with Applications 15 (8), 685-700= , 2008	15	2008
Tuning parameter selec= tion and various good fitting characteristics for the Liu-Type estimator in= linear regression H Yang, J Xu Communications in Statistics=E2=80=94Theory and Methods 37 (20= ), 3204-3215, 2008	7	2008
On the time v= alue of absolute ruin for a multi-layer compound Poisson model under intere= st force H Yang, Z Zhang, C Lan Statistics & probability letters 78 (13), 1835-1845, 2008	28	= 2008
=E7=BA=BF=E6=80=A7=E6=B7=B7=E5= =90=88=E6=A8=A1=E5=9E=8B=E5=8F=82=E6=95=B0=E7=9A=84=E9=83=A8=E5=88=86=E5=B2= =AD=E5=9E=8B=E8=B0=B1=E5=88=86=E8=A7=A3=E4=BC=B0=E8=AE=A1 =E6=9D=A8=E8=99=8E=EF=BC=8C =E9=BB=8E=E9=9B=85=E8=8E=B2 =E5=BA=94=E7=94=A8=E6=A6=82=E7=8E=87=E7=BB=9F=E8=AE=A1 24= (3), 289-296, 2008		2008
Gerber=E2=80=93Shiu discounted penalty function in a Sparre A= ndersen model with multi-layer dividend strategy = H Yang, Z Zhang Insurance: Mathematics and Econ= omics 42 (3), 984-991, 2008	53	2008
Wei= ghted UDV=E2=88=97-decomposition and weighted spectral decomposition for re= ctangular matrices and their applications H Yang,= H Li Applied mathematics and computation 198 (= 1), 150-162, 2008	12
Matrix left = symmetry factor and its applications in generalized inverses AT, S (2, 4) H Yang, D Liu, J Xu Ap= plied mathematics and computation 197 (2), 836-843, = 2008	12	2008
=E7=94=9F=E9=95=BF=E6=9B=B2=E7=BA=BF=E6=A8=A1=E5= =9E=8B=E4=B8=8B=E7=9A=84=E7=BB=9F=E4=B8=80=E6=9C=89=E5=81=8F=E4=BC=B0=E8=AE= =A1 =E9=83=91=E5=BD=AD=E4=B8=B9=EF=BC=8C =E6=9D= =A8=E8=99=8E =E5=B7=A5=E7=A8=8B=E6=95=B0=E5=AD= =A6=E5=AD=A6=E6=8A=A5 25 (2), 353-357, 2008		2008
Some optimal dividend problems for a = surplus process with interest =E6=9D=A8=E8=99=8E= =EF=BC=8C =E8=80=BF=E6=96=87=E5=A9=B7 =E9=87=8D= =E5=BA=86=E5=A4=A7=E5=AD=A6=E5=AD=A6=E6=8A=A5: =E8=8B=B1=E6=96=87=E7=89=88 = 7 (2), 132-136, 2008	1
Reverse Ord= er Laws for Weighted Least-squares g-inverses and Weighted Minimun Norm g-i= nverses of the Product of Two H Yang, D Liu, X Ch= ang Advances in Matrix Theory and Its Applicati= ons: Proceedings of the Eighth =E2=80=A6, 2008<= /span>		2008
=E5=9F=BA=E4=BA=8E APARCH-Lapl= ace =E6=A8=A1=E5=9E=8B=E7=9A=84 VaR =E5=92=8C CVaR =E6=96=B9=E6=B3=95=E5=AE=8B=E4=B8=BD=E5=A8=9F=EF=BC=8C =E6=9D=A8=E8=99= =8E =E7=BB=9F=E8=AE=A1=E4=B8=8E=E5=86=B3=E7=AD= =96 24 (14), 16-19, 2008	8<= /td>	2008
Weigh= ted polar decomposition and WGL partial ordering of rectangular complex mat= rices H Yang, H Li SI= AM journal on matrix analysis and applications 30 (2), 898-924, 2008	29	2008
=E6=B3=9B=E6=AD=A3=E5=AE=9A=E7=9F= =A9=E9=98=B5=E7=9A=84=E8=BF=9B=E4=B8=80=E6=AD=A5=E7=A0=94=E7=A9=B6 =E6=9D=A8=E8=99=8E=EF=BC=8C =E9=82=B5=E5=8D=8E =E6=95=B0=E5=AD=A6=E7=9A=84=E5=AE=9E=E8=B7=B5=E4=B8=8E=E8= =AE=A4=E8=AF=86 38 (3), 114-122, 2008	1	2008
The research on two kinds of restricted biased estimators based o= n mean squared error matrix H Yang, C Zhang = Communications in Statistics-Theory and Methods 37 (= 1), 70-80, 2007	9	2007
The conditional= ridge-type estimation in singular linear model with linear equality restri= ctions C Zhang, H Yang Statistics 41 (6), 485-494, 2007	6	= 2007
=E8=8C=83=E6=95=B0=E7=B1=BB Kantorovich =E4=B8=8D=E7=AD=89=E5=BC=8F= =E7=9A=84=E6=96=B0=E7=BB=93=E6=9E=9C =E9=A9=AC=E9= =93=81=E4=B8=B0=EF=BC=8C =E6=9D=A8=E8=99=8E =E5= =BA=94=E7=94=A8=E6=A6=82=E7=8E=87=E7=BB=9F=E8=AE=A1 23 (3), 247-253, 2007		2007<= /td>
Ridge e= stimation to the restricted linear model Z Zhong,= H Yang Communications in Statistics=E2=80=94Th= eory and Methods 36 (11), 2099-2115, 2007	57	2007
<= a href=3D"https://scholar.google.com/citations?view_op=3Dview_citation&= hl=3Dzh-CN&user=3D_Nr93scAAAAJ&cstart=3D200&pagesize=3D100&= sortby=3Dpubdate&citation_for_view=3D_Nr93scAAAAJ:YFjsv_pBGBYC" class= =3D"gsc_a_at">Estimation in singular linear models with stochastic linear r= estrictions J Xu, H Yang Communications in Statistics=E2=80=94Theory and Methods 36 (10), 1945-1= 951, 2007	16	2007
A New Relative Effici= ency in Parameter Estimation for Linear Model H Y= ang, ZL Chen Journal of Chongqing University 6 = (2), 141-145, 2007		2007
=E6=A6=82 =E7=8E=87 =E8=AE=BA =E4=B8=8E =E6=95=B0 =E7=90=86 = =E7=BB=9F =E8=AE=A1 =E6=9D=A8=E8=99=8E=EF=BC=8C = =E5=88=98=E7=90=BC=E8=8D=AA=EF=BC=8C =E9=92=9F=E6=B3=A2 =E9=87=8D =E5=BA=86: =E9=87=8D =E5=BA=86, 2= 007	3*	2007
=E5=B9=BF=E4=B9=89=E5=B2=AD= =E5=9E=8B=E4=BC=B0=E8=AE=A1=E5=B2=AD=E5=8F=82=E6=95=B0=E7=9A=84=E7=A1=AE=E5= =AE=9A=E6=96=B9=E6=B3=95 =E4=B8=A5=E7=BE=BF=E9=B9= =8F=EF=BC=8C =E6=9D=A8=E8=99=8E =E9=87=8D=E5=BA= =86=E5=A4=A7=E5=AD=A6=E5=AD=A6=E6=8A=A5 30 (12), 98-102, 2007	5	2007
=E6=A4=AD=E7=90=83=E7=BA=A6=E6=9D=9F=E4=B8=8B= =E7=BA=BF=E6=80=A7=E6=A8=A1=E5=9E=8B=E7=9A=84=E5=BC=BA=E5=BD=B1=E5=93=8D=E5= =88=86=E6=9E=90 =E6=9D=A8=E8=8E=B2=EF=BC=8C =E6= =9D=A8=E8=99=8E =E5=B7=A5=E7=A8=8B=E6=95=B0=E5= =AD=A6=E5=AD=A6=E6=8A=A5 24 (1), 60-64, 2007<= /div>	4	2007
=E5=BA=94=E7=94=A8=E6=95=B0=E7=90=86=E7=BB=9F=E8=AE=A1=E6=9D=A8=E8=99=8E=EF=BC=8C =E9=92=9F=E6=B3=A2=EF=BC=8C= =E5=88=98=E7=90=BC=E8=8D=AA =E6=B8=85=E5=8D=8E= =E5=A4=A7=E5=AD=A6=E5=87=BA=E7=89=88=E7=A4=BE, 2006<= /span>	36	2006
=E5=9F=BA=E4=BA=8E {2}-=E9=80=86=E7=9A=84=E5=B9=BF= =E4=B9=89=E9=80=86=E7=9A=84=E6=9E=84=E9=80=A0=E5=8F=8A=E5=85=B6=E6=80=A7=E8= =B4=A8 =E9=A9=AC=E6=8D=B7=EF=BC=8C =E6=9D=A8=E8= =99=8E =E9=AB=98=E6=A0=A1=E5=BA=94=E7=94=A8=E6= =95=B0=E5=AD=A6=E5=AD=A6=E6=8A=A5: A =E8=BE=91 21 (2), 197-203, 2006	4	2006
Bayes =E4=BC=B0=E8=AE=A1=E7=9A=84=E8= =BF=9B=E4=B8=80=E6=AD=A5=E7=A0=94=E7=A9=B6=E5=8F=8A=E5=85=B6 Pitman =E6=9C= =80=E4=BC=98=E6=80=A7 =E9=A9=AC=E9=93=81=E4=B8=B0= =EF=BC=8C =E6=9D=A8=E8=99=8E =E5=BA=94=E7=94=A8= =E6=95=B0=E5=AD=A6=E5=AD=A6=E6=8A=A5 29 (3), 428-435= , 2006	2	2006
=E9=9D=9E=E8=B4=9F=E5=AE=9A=E9=98=B5=E5=85=83=E7= =B4=A0=E7=9A=84=E6=89=B0=E5=8A=A8=E5=92=8C=E6=AD=A3=E5=AE=9A=E5=AE=8C=E5=A4= =87=E5=8C=96 =E9=A9=AC=E6=8D=B7=EF=BC=8C =E6=9D= =A8=E8=99=8E =E6=95=B0=E5=AD=A6=E8=BF=9B=E5=B1= =95 35 (3), 275-284, 2006		2006
=E5=85=B3=E4=BA=8E James-Stein =E4=BC=B0=E8=AE=A1=E7= =9A=84=E5=B0=8F=E6=A0=B7=E6=9C=AC=E6=80=A7=E8=B4=A8 =E9=A9=AC=E9=93=81=E4=B8=B0=EF=BC=8C =E6=9D=A8=E8=99=8E =E6=95=B0=E5=AD=A6=E7=9A=84=E5=AE=9E=E8=B7=B5=E4=B8=8E=E8=AE= =A4=E8=AF=86 36 (10), 119-124, 2006		2006
3	2005
Outlier mining b= ased on principal component estimation H Yang, T = Yang Acta Mathematicae Applicatae Sinica 21 (2)= , 303-310, 2005	1	2005
=E5=85=B3=E4=BA= =8E=E7=9F=A9=E9=98=B5=E7=89=B9=E5=BE=81=E5=80=BC=E7=9A=84=E6=89=B0=E5=8A=A8= =E8=B4=BE=E4=B8=BD=E6=9D=B0=EF=BC=8C =E6=9D=A8= =E8=99=8E =E9=87=8D=E5=BA=86=E5=A4=A7=E5=AD=A6= =E5=AD=A6=E6=8A=A5 28 (4), 113-115, 2005	1	2005
=E6=8A=93=E5=A5=BD=E8=B5=9B=E5=8C=BA=E6=95=B0=E5=AD=A6=E5=BB=BA= =E6=A8=A1=E7=AB=9E=E8=B5=9B =E4=BF=83=E8=BF=9B=E5=B7=A5=E7=A7=91=E6=95=B0= =E5=AD=A6=E6=95=99=E5=AD=A6=E6=94=B9=E9=9D=A9 =E4= =BB=BB=E5=96=84=E5=BC=BA=EF=BC=8C =E6=9D=A8=E8=99=8E =E5=A4=A7=E5=AD=A6=E6=95=B0=E5=AD=A6 21 (5), 22-24, 2005	3	2005
	2= 005
=E5=9F=BA=E4=BA=8E=E5=B2=AD=E5=9E=8B=E7=BB=84=E5=90=88=E4=B8=BB=E6=88=90= =E5=88=86=E4=BC=B0=E8=AE=A1=E7=9A=84=E5=BD=B1=E5=93=8D=E5=87=BD=E6=95=B0 =E8=AE=B8=E6=B5=B7=E9=9C=9E=EF=BC=8C =E6=9D=A8=E8= =99=8E =E4=B8=AD=E5=9B=BD=E7=8E=B0=E5=9C=BA=E7= =BB=9F=E8=AE=A1=E7=A0=94=E7=A9=B6=E4=BC=9A=E7=AC=AC 12 =E5=B1=8A=E5=AD=A6= =E6=9C=AF=E5=B9=B4=E4=BC=9A=E8=AE=BA=E6=96=87=E9=9B=86, 2005		2005
=E5=85=B3=E4=BA=8E=E6= =B3=9B=E5=B2=AD=E4=BC=B0=E8=AE=A1=E7=9A=84=E5=BD=B1=E5=93=8D=E5=88=86=E6=9E= =90 =E6=9D=A8=E8=8E=B2=EF=BC=8C =E6=9D=A8=E8=99= =8E =E4=B8=AD=E5=9B=BD=E7=8E=B0=E5=9C=BA=E7=BB= =9F=E8=AE=A1=E7=A0=94=E7=A9=B6=E4=BC=9A=E7=AC=AC 12 =E5=B1=8A=E5=AD=A6=E6= =9C=AF=E5=B9=B4=E4=BC=9A=E8=AE=BA=E6=96=87=E9=9B=86,= 2005		2005
=E6=A0=B8=E5=AF=86=E5=BA= =A6=E4=BC=B0=E8=AE=A1=E5=9C=A8=E9=A2=84=E6=B5=8B=E9=A3=8E=E9=99=A9=E4=BB=B7= =E5=80=BC=E4=B8=AD=E7=9A=84=E5=BA=94=E7=94=A8 =E5= =94=90=E6=9E=97=E4=BF=8A=EF=BC=8C =E6=9D=A8=E8=99=8E=EF=BC=8C =E5=BC=A0=E6= =B4=AA=E9=98=B3 =E6=95=B0=E5=AD=A6=E7=9A=84=E5= =AE=9E=E8=B7=B5=E4=B8=8E=E8=AE=A4=E8=AF=86 35 (10), 29-35, 2005	13	2005
2005
=E5=9F=BA=E4=BA=8E=E5=A5=97=E5=88=A9=E5=AE=9A=E4= =BB=B7=E7=90=86=E8=AE=BA=E7=9A=84=E5=A4=9A=E5=9B=A0=E7=B4=A0=E5=AE=9A=E4=BB= =B7=E6=A8=A1=E5=9E=8B=E7=9A=84=E5=AE=9E=E8=AF=81=E7=A0=94=E7=A9=B6 =E6=9D=A8=E8=99=8E=EF=BC=8C =E7=A8=8B=E5=A8=9F =E4=B8=AD=E5=9B=BD=E7=8E=B0=E5=9C=BA=E7=BB=9F=E8=AE=A1=E7= =A0=94=E7=A9=B6=E4=BC=9A=E7=AC=AC 12 =E5=B1=8A=E5=AD=A6=E6=9C=AF=E5=B9=B4= =E4=BC=9A=E8=AE=BA=E6=96=87=E9=9B=86, 2005		2005
Adaptive unified biased estimators of= parameters in linear model H Yang, L Zhu Acta Mathematicae Applicatae Sinica 20 (3), 425-432, 2004	6	2004
=E6=95=B0=E7=90=86=E7=BB= =9F=E8=AE=A1 =E6=9D=A8=E8=99=8E=EF=BC=8C =E5=88= =98=E7=90=BC=E8=8D=AA=EF=BC=8C =E9=92=9F=E6=B3=A2 =E9=AB=98=E7=AD=89=E6=95=99=E8=82=B2=E5=87=BA=E7=89=88=E7=A4=BE, 2004	93	<= span class=3D"gsc_a_h gsc_a_hc gs_ibl">2004
=E5=9F=BA=E4=BA=8E=E9=A2=84=E6= =B5=8B=E7=9A=84=E5=BA=8F=E5=88=97=E5=BC=82=E5=B8=B8=E6=95=B0=E6=8D=AE=E6=8C= =96=E6=8E=98 =E6=9D=A8=E8=99=8E=EF=BC=8C =E7=8E= =8B=E4=BC=9A=E7=90=A6=EF=BC=8C =E7=A8=8B=E4=BB=A3=E6=9D=B0 =E8=AE=A1=E7=AE=97=E6=9C=BA=E7=A7=91=E5=AD=A6 31 (4), 117-119<= span class=3D"gs_oph">, 2004	8	2004
=E5=9F=BA=E4=BA=8E=E4= =B8=8D=E5=AE=8C=E5=85=A8=E6=95=B0=E6=8D=AE=E7=9A=84=E5=BC=82=E5=B8=B8=E6=8C= =96=E6=8E=98=E7=AE=97=E6=B3=95=E7=A0=94=E7=A9=B6 = =E6=9D=A8=E8=99=8E=EF=BC=8C =E9=92=9F=E9=9C=87=EF=BC=8C =E7=A8=8B=E4=BB=A3= =E6=9D=B0 =E8=AE=A1=E7=AE=97=E6=9C=BA=E7=A0=94= =E7=A9=B6=E4=B8=8E=E5=8F=91=E5=B1=95 41 (9), 1532-1539, 2004	1	2004
=E6=B7=B1=E6=B2=AA=E8=82=A1=E5=B8=82=E6=94=B6=E7= =9B=8A=E7=8E=87=E5=88=86=E5=B8=83=E7=89=B9=E5=BE=81=E7=9A=84=E7=BB=9F=E8=AE= =A1=E5=88=86=E6=9E=90 =E5=94=90=E6=9E=97=E4=BF=8A= =EF=BC=8C =E6=9D=A8=E8=99=8E =E6=95=B0=E7=90=86= =E7=BB=9F=E8=AE=A1=E4=B8=8E=E7=AE=A1=E7=90=86 23 (5), 1-4, 2004	18	2004
=E5=BA=8F=E5=88=97=E6= =95=B0=E6=8D=AE=E6=8C=96=E6=8E=98=E7=9A=84=E6=A8=A1=E5=9E=8B=E5=92=8C=E7=AE= =97=E6=B3=95=E7=A0=94=E7=A9=B6 =E6=9D=A8=E8=99=8E= =E9=87=8D=E5=BA=86: =E9=87=8D=E5=BA=86=E5=A4= =A7=E5=AD=A6, 2003	1
=E5=9B=A0=E5= =AD=90=E5=88=86=E6=9E=90=E6=B3=95=E5=9C=A8=E5=8C=BA=E5=8E=BF=E7=BB=8F=E6=B5= =8E=E7=BB=BC=E5=90=88=E6=8C=87=E6=A0=87=E8=AF=84=E6=9E=90=E4=B8=AD=E7=9A=84= =E5=BA=94=E7=94=A8 =E5=94=90=E6=9E=97=E4=BF=8A=EF= =BC=8C =E6=9D=A8=E8=99=8E =E6=95=B0=E7=90=86=E7= =BB=9F=E8=AE=A1=E4=B8=8E=E7=AE=A1=E7=90=86 22 (5), 24-29, 2003	39	2003
21	2003
=E9=87=8D=E5=BA=86= =E5=B8=82=E4=BA=BA=E5=8F=A3=E5=88=86=E5=B8=83=E6=A8=A1=E5=9E=8B=E4=B8=8E=E7= =89=B9=E5=BE=81=E5=88=86=E6=9E=90 =E5=BC=A0=E6=B4= =AA=E9=98=B3=EF=BC=8C =E6=9D=A8=E8=99=8E =E4=BA= =BA=E5=8F=A3=E4=B8=8E=E7=BB=8F=E6=B5=8E 2, 53-58, 20= 03	6	2003
Laplace =E5=88=86=E5=B8=83=E5=87=BD=E6=95=B0=E7= =9A=84=E6=80=A7=E8=B4=A8=E5=8F=8A=E5=85=B6=E5=9C=A8=E8=AF=81=E5=88=B8=E5=B8= =82=E5=9C=BA=E4=B8=AD=E7=9A=84=E5=BA=94=E7=94=A8 = =E5=94=90=E6=9E=97=E4=BF=8A=EF=BC=8C =E6=9D=A8=E8=99=8E =E5=8A=A0=E5=85=A5 WTO =E5=92=8C=E4=B8=AD=E5=9B=BD=E7=A7=91=E6=8A= =80=E4=B8=8E=E5=8F=AF=E6=8C=81=E7=BB=AD=E5=8F=91=E5=B1=95=E2=80=94=E2=80=94= =E6=8C=91=E6=88=98=E4=B8=8E=E6=9C=BA=E9=81=87, =E8=B4=A3=E4=BB=BB=E5=92=8C= =E5=AF=B9=E7=AD=96 (=E4=B8=8A=E5=86=8C), 2002=		2002
=E5=9F=BA=E4=BA=8E=E5=B2=AD=E4=BC=B0= =E8=AE=A1=E7=9A=84=E6=9C=80=E4=BC=98=E9=A2=84=E6=B5=8B=E4=B8=8E=E7=BB=8F=E5= =85=B8=E9=A2=84=E6=B5=8B=E7=9A=84=E6=9C=80=E4=BC=98=E6=80=A7=E5=88=A4=E5=88= =AB =E6=9D=A8=E5=A9=B7=EF=BC=8C =E6=9D=A8=E8=99= =8E=EF=BC=8C =E5=BC=A0=E6=B4=AA=E9=98=B3 =E9=87= =8D=E5=BA=86=E5=A4=A7=E5=AD=A6=E5=AD=A6=E6=8A=A5 25 (6), 56, 2002	7	2002
=E5=A4=8D=E4=BA=9A=E6=AD=A3=E5=AE=9A=E7= =9F=A9=E9=98=B5=E7=9A=84=E5=87=A0=E4=B8=AA=E8=A1=8C=E5=88=97=E5=BC=8F=E4=B8= =8D=E7=AD=89=E5=BC=8F =E5=94=90=E6=9E=97=E4=BF=8A= =EF=BC=8C =E6=9D=A8=E8=99=8E =E7=BA=AF=E7=B2=B9= =E6=95=B0=E5=AD=A6=E4=B8=8E=E5=BA=94=E7=94=A8=E6=95=B0=E5=AD=A6 18 (3), 278= -281, 2002	2	2002
On Generalized Pot= thoff-Roy Estimator Y Hu, T Fengmei Journal of Biomathematics 16 (3), 266-274, 2001		2001
=E9=87=8D=E5=BA=86= =E5=B7=A5=E4=B8=9A=E6=99=AE=E6=9F=A5=E4=B8=AD=E7=BB=8F=E6=B5=8E=E6=8C=87=E6= =A0=87=E7=9A=84=E5=85=B3=E7=B3=BB =E6=9D=A8=E8=99= =8E=EF=BC=8C =E9=AB=98=E8=91=86=E6=97=BA=EF=BC=8C =E6=9B=B9=E8=93=93=EF=BC= =8C =E9=99=88=E7=81=BF=E5=A8=81 =E9=87=8D=E5=BA= =86=E5=A4=A7=E5=AD=A6=E5=AD=A6=E6=8A=A5=E7=A4=BE=E4=BC=9A=E7=A7=91=E5=AD=A6= =E7=89=88 7 (1), 12-14, 2001	1	2001
=E9= =87=8D=E5=BA=86=E5=9B=BD=E6=9C=89=E4=BC=81=E4=B8=9A=E6=99=AE=E6=9F=A5=E6=8A= =80=E6=9C=AF=E6=8C=87=E6=A0=87=E7=9A=84=E4=B8=BB=E5=9B=A0=E7=B4=A0=E5=88=86= =E6=9E=90 =E6=9D=A8=E8=99=8E=EF=BC=8C =E9=AB=98= =E8=91=86=E6=97=BA=EF=BC=8C =E6=9B=B9=E8=93=93=EF=BC=8C =E9=99=88=E7=81=BF= =E5=A8=81 =E9=87=8D=E5=BA=86=E5=A4=A7=E5=AD=A6= =E5=AD=A6=E6=8A=A5=E7=A4=BE=E4=BC=9A=E7=A7=91=E5=AD=A6=E7=89=88 7 (2), 9, 2001	<= /a>	2001<= /span>
= =E4=B8=80=E7=B1=BB=E5=88=86=E5=B8=83=E5=8F=8A=E7=9B=B8=E5=85=B3=E9=97=AE=E9= =A2=98=E6=8E=A2=E8=AE=A8 =E6=9D=A8=E8=99=8E = =E9=87=8D=E5=BA=86=E5=A4=A7=E5=AD=A6=E5=AD=A6=E6=8A= =A5 24 (2), 118-121, 2001		2001
=E9=87=8D=E5=BA=86=E5=9B=BD=E6=9C=89=E4=BC=81=E4=B8= =9A=E5=8A=B3=E5=8A=A8=E5=8A=9B=E8=B5=84=E6=BA=90=E6=9E=84=E6=88=90=E7=9A=84= =E5=BD=B1=E5=93=8D=E5=88=86=E6=9E=90 =E6=9D=A8=E8= =99=8E=EF=BC=8C =E6=9B=B9=E8=93=93=EF=BC=8C =E9=AB=98=E8=91=86=E6=97=BA=EF= =BC=8C =E9=99=88=E7=81=BF=E5=A8=81 =E9=87=8D=E5= =BA=86=E5=A4=A7=E5=AD=A6=E5=AD=A6=E6=8A=A5=E7=A4=BE=E4=BC=9A=E7=A7=91=E5=AD= =A6=E7=89=88 6 (1), 5-8, 2000		2000
=E6=88=91=E5=9B=BD=E5=B7=A5=E4=B8=9A=E4=BC=81=E4=B8= =9A=E5=8A=B3=E5=8A=A8=E5=8A=9B=E8=B5=84=E6=BA=90=E6=9E=84=E6=88=90=E7=9A=84= =E5=BD=B1=E5=93=8D=E5=88=86=E6=9E=90 =E6=9D=A8=E8= =99=8E=EF=BC=8C =E6=9B=B9=E8=93=93 =E9=9D=A2=E5= =90=91 21 =E4=B8=96=E7=BA=AA=E7=9A=84=E7=A7=91=E6=8A=80=E8=BF=9B=E6=AD=A5= =E4=B8=8E=E7=A4=BE=E4=BC=9A=E7=BB=8F=E6=B5=8E=E5=8F=91=E5=B1=95 (=E4=B8=8B= =E5=86=8C), 1999		1999
{2}=E2=80=94=E2=80=94=E9=80=86=E7=9A=84=E7=89=B9=E6=80=A7=E5=8F= =8A=E8=8B=A5=E5=B9=B2=E5=88=86=E5=9D=97=E9=97=AE=E9=A2=98 =E8=91=A3=E5=BF=97=E6=B8=85=EF=BC=8C =E6=9D=A8=E8=99=8E =E7=BA=AF=E7=B2=B9=E6=95=B0=E5=AD=A6=E4=B8=8E=E5=BA=94=E7= =94=A8=E6=95=B0=E5=AD=A6 14 (2), 82-87, 1998<= /div>	1	1998
=E5=85=B3=E4=BA=8E=E7=9F=A9=E9=98=B5=E4=BA=8C=E6=AC=A1=E5=9E= =8B=E6=9E=81=E5=80=BC=E7=9A=84=E8=8B=A5=E5=B9=B2=E7=BB=93=E6=9E=9C =E6=9D=A8=E8=99=8E =E6=95=B0= =E5=AD=A6=E6=9D=82=E5=BF=97 17 (3), 306-310, 1997	2	1997
=E4=B8=80=E7=B1=BB=E6=96=B0=E7=9A=84=E5=8F=82=E6=95=B0= =E4=BC=B0=E8=AE=A1=E4=BF=A1=E6=81=AF=E6=8D=9F=E5=A4=B1=E5=BA=A6=E9=87=8F =E6=9D=A8=E8=99=8E=EF=BC=8C =E8=91=A3=E5=BF=97=E6= =B8=85 =E9=87=8D=E5=BA=86=E4=BA=A4=E9=80=9A=E5= =A4=A7=E5=AD=A6=E5=AD=A6=E6=8A=A5 (=E8=87=AA=E7=84=B6=E7=A7=91=E5=AD=A6=E7= =89=88) 15 (S1), 43, 1996		1996
On the stability of biased estimates and the regular= ization method H Yang Journal of statistical planning and inference 52 (1), 67-75, 1996	2	1996
Efficiency matrix and the partial order= ing of estimate H Yang Communications in Statistics-Theory and Methods 25 (2), 457-468, 1996	19	1996
Matrix norm versions of the Kanto= rovich inequality and its applications Y Hu = Applied Mathematics 10 (2), 133-140, 1995	2	1995
	1995
=E8=BF=BD=E5=8A=A0=E6=95=B0=E6=8D= =AE=E5=AF=B9=E6=9D=A1=E4=BB=B6=E6=8C=87=E6=95=B0=E7=9A=84=E5=BD=B1=E5=93=8D= =E6=9D=A8=E8=99=8E=EF=BC=8C =E7=8E=8B=E6=9D=BE= =E6=A1=82=EF=BC=8C =E5=BC=A0=E5=BF=A0=E5=8D=A0 = =E5=BA=94=E7=94=A8=E6=A6=82=E7=8E=87=E7=BB=9F=E8=AE=A1 11 (3), 225-234, 1995	2	1995
Linear estimators under Pitma= n nearness criterion =E7=8E=8B=E6=9D=BE=E6=A1=82= =EF=BC=8C =E6=9D=A8=E8=99=8E Chinese Science Bu= lletin 40 (23), 1944-1948, 1995	1995=
= =E5=A4=9A=E5=85=83=E7=BA=BF=E6=80=A7=E6=A8=A1=E5=9E=8B=E4=BC=B0=E8=AE=A1=E7= =9A=84=E8=AF=AF=E5=B7=AE=E7=95=8C=E5=8F=8A=E4=B8=80=E7=B1=BB=E6=A3=80=E9=AA= =8C=E7=BB=9F=E8=AE=A1=E9=87=8F =E6=9D=A8=E8=99=8E= =E9=87=8D=E5=BA=86=E4=BA=A4=E9=80=9A=E5=A4=A7= =E5=AD=A6=E5=AD=A6=E6=8A=A5 (=E8=87=AA=E7=84=B6=E7=A7=91=E5=AD=A6=E7=89=88)= 13 (S1), 6-11, 1994		1994
=E5=9B=9E=E5=BD=92=E6=A8=A1=E5=9E=8B=E4=B8=AD=E4=B8=AD=E5=BF= =83=E5=8C=96=E5=88=BB=E5=BA=A6=E5=8F=98=E6=8D=A2=E5=AF=B9=E5=A4=8D=E7=BA=BF= =E6=80=A7=E5=BA=A6=E9=87=8F=E7=9A=84=E5=BD=B1=E5=93=8D< =E8=8B=B1=E6=96= =87 =E7=8E=8B=E6=9D=BE=E6=8C=82=EF=BC=8C =E6=9D= =A8=E8=99=8E =E6=95=B0=E7=90=86=E7=BB=9F=E8=AE= =A1=E4=B8=8E=E5=BA=94=E7=94=A8=E6=A6=82=E7=8E=87 9 (2), 9-17, 1994		1994
Pitman =E5=87= =86=E5=88=99=E4=B8=8B=E7=9A=84=E7=BA=BF=E6=80=A7=E4=BC=B0=E8=AE=A1 =E7=8E=8B=E6=9D=BE=E6=A1=82=EF=BC=8C =E6=9D=A8=E8=99=8E =E7=A7=91=E5=AD=A6=E9=80=9A=E6=8A=A5 39 (16), 14= 44-1447, 1994	5	1994
=E7=BB=9F=E8=AE= =A1=E5=AD=A6=E4=B8=AD=E7=9A=84=E8=8B=A5=E5=B9=B2=E7=9F=A9=E9=98=B5=E7=A0=94= =E7=A9=B6 =E7=8E=8B=E6=9D=BE=E6=A1=82=EF=BC=8C = =E6=9D=A8=E8=99=8E =E9=87=8D=E5=BA=86=E4=BA=A4= =E9=80=9A=E5=A4=A7=E5=AD=A6=E5=AD=A6=E6=8A=A5 (=E8=87=AA=E7=84=B6=E7=A7=91= =E5=AD=A6=E7=89=88) 12 (3), 17, 1993	3	1993
=E7=9F=A9=E9=98=B5=E7=9A=84=E6=B3=9B=E6=AD=A3=E5=AE=9A=E4=B8=8E=E5=B9= =BF=E4=B9=89=E9=80=86=E5=81=8F=E5=BA=8F =E6=9D=A8= =E8=99=8E =E7=B3=BB=E7=BB=9F=E7=A7=91=E5=AD=A6= =E4=B8=8E=E6=95=B0=E5=AD=A6 13 (3), 270-275, 1993	21	1993
=E6=9D=83=E5=9B=9E=E5=BD=92=E4=B8=8E=E4=B8=80=E8=88=AC= =E5=BD=B1=E5=93=8D=E5=BA=A6=E9=87=8F =E6=9D=A8=E8= =99=8E =E5=BA=94=E7=94=A8=E6=95=B0=E5=AD=A6=E5= =92=8C=E5=8A=9B=E5=AD=A6 13 (9), 843-847, 1992	3	1992
BLUE =E7=9A=84=E6=95=8F=E6=84=9F=E6=80=A7=E5=88=86=E6=9E=90= =E6=9D=A8=E8=99=8E = =E6=95=B0=E5=AD=A6=E6=9D=82=E5=BF=97 12 (1), 66-74, = 1992		1992
=E4=B8=A4=E6=AD=A5=E4=BC= =B0=E8=AE=A1=E6=95=88=E7=8E=87=E7=9A=84=E8=8B=A5=E5=B9=B2=E6=B3=A8=E8=AE=B0= =E6=9D=A8=E8=99=8E = =E9=87=8D=E5=BA=86=E4=BA=A4=E9=80=9A=E5=A4=A7=E5=AD=A6=E5=AD=A6=E6=8A=A5 (= =E8=87=AA=E7=84=B6=E7=A7=91=E5=AD=A6=E7=89=88) 10 (2), 6, 1991		1991
Weighted Regressio= n and the General Influence Measure =E6=9D=A8=E8= =99=8E =E5=BA=94=E7=94=A8=E6=95=B0=E5=AD=A6 1, 1991	<= /a>	1991<= /span>
= =E5=BC=BA=E5=BD=B1=E5=93=8D=E7=82=B9=E7=9A=84=E4=B8=80=E7=A7=8D=E6=96=B0=E5= =BA=A6=E9=87=8F =E6=9D=A8=E8=99=8E =E6=95=B0=E7=90=86=E7=BB=9F=E8=AE=A1=E4=B8=8E=E5=BA=94=E7=94= =A8=E6=A6=82=E7=8E=87 6 (2), 211-219, 1991	3	1991
<= a href=3D"https://scholar.google.com/citations?view_op=3Dview_citation&= hl=3Dzh-CN&user=3D_Nr93scAAAAJ&cstart=3D300&pagesize=3D100&= sortby=3Dpubdate&citation_for_view=3D_Nr93scAAAAJ:3fE2CSJIrl8C" class= =3D"gsc_a_at">=E5=85=B3=E4=BA=8E=E5=9B=9E=E5=BD=92=E7=B3=BB=E6=95=B0=E7=9A= =84=E6=B3=9B=E5=B2=AD=E4=BC=B0=E8=AE=A1=E7=B1=BB = =E6=9D=A8=E8=99=8E =E9=87=8D=E5=BA=86=E4=BA=A4= =E9=80=9A=E5=AD=A6=E9=99=A2=E5=AD=A6=E6=8A=A5 10 (003), 42-48, 1991	6	1991
=E6=9D=A1=E4=BB=B6=E6=95=B0, =E8=B0=B1= =E8=8C=83=E6=95=B0=E4=B8=8E=E4=BC=B0=E8=AE=A1=E7=B2=BE=E5=BA=A6 =E6=9D=A8=E8=99=8E=EF=BC=8C =E7=8E=8B=E6=9D=BE=E6=A1=82 =E5=BA=94=E7=94=A8=E6=A6=82=E7=8E=87=E7=BB=9F=E8=AE= =A1 7 (4), 337-343, 1991	11=	1991
=E5= =A4=A7=E5=9E=8B=E5=9B=9E=E5=BD=92=E7=B3=BB=E7=BB=9F=E7=9A=84=E7=BA=BF=E6=80= =A7=E4=BC=B0=E8=AE=A1 =E6=9D=A8=E8=99=8E =E9=87=8D=E5=BA=86=E4=BA=A4=E9=80=9A=E5=A4=A7=E5=AD=A6= =E5=AD=A6=E6=8A=A5 (=E8=87=AA=E7=84=B6=E7=A7=91=E5=AD=A6=E7=89=88) 9 (3), 5= 0, 1990	1	1990
=E7=BA=BF=E6=80=A7=E7=BA= =A6=E6=9D=9F=E4=B8=8B=E5=8F=82=E6=95=B0=E4=BC=B0=E8=AE=A1=E7=9A=84=E7=81=B5= =E6=95=8F=E6=80=A7 =E6=9D=A8=E8=99=8E =E9=87=8D=E5=BA=86=E4=BA=A4=E9=80=9A=E5=A4=A7=E5=AD=A6=E5= =AD=A6=E6=8A=A5 (=E8=87=AA=E7=84=B6=E7=A7=91=E5=AD=A6=E7=89=88) 9 (2), 98, 1990	<= /a>	1990<= /span>
A= brief proof on the generalized variance bound of the relative efficiency i= n statistics H Yang C= ommunications in Statistics-Theory and Methods 19 (12), 4587-4590, 1990	5	1990
=E4=B8=A4=E7=B1=BB=E6=96=B0=E7=9A= =84=E5=B9=BF=E4=B9=89 Kantorovich =E4=B8=8D=E7=AD=89=E5=BC=8F=E5=8F=8A=E5= =85=B6=E5=BA=94=E7=94=A8 =E6=9D=A8=E8=99=8E = =E7=A7=91=E5=AD=A6=E9=80=9A=E6=8A=A5 35 (13), 965-96= 7, 1990	4	1990
=E5=9F=8E=E5=B8=82=E5= =8C=BA=E5=9F=9F=E4=BA=A4=E9=80=9A=E7=9A=84=E5=AE=8F=E8=A7=82=E7=B3=BB=E7=BB= =9F=E6=A8=A1=E5=9E=8B=E5=88=86=E6=9E=90 =E6=9D=A8= =E8=99=8E =E5=A4=A7=E8=87=AA=E7=84=B6=E6=8E=A2= =E7=B4=A2 9 (3), 89-93, 1990		1990
=E5=BC=BA=E5=BD=B1=E5=93=8D=E7=9A=84=E5=88=86=E5=B8= =83=E4=B8=8E=E7=A8=B3=E5=AE=9A=E6=80=A7=E5=BA=A6=E9=87=8F =E6=9D=A8=E8=99=8E =E6=95=B0=E5=AD=A6= =E7=A0=94=E7=A9=B6=E4=B8=8E=E8=AF=84=E8=AE=BA: =E8=8B=B1=E6=96=87=E7=89=88 = 10 (3), 393-398, 1990	7	1990
=E5=85= =B3=E4=BA=8E Rayleigh =E5=95=86=E4=B8=8A=E7=95=8C=E7=9A=84=E8=8B=A5=E5=B9= =B2=E6=8E=A8=E5=B9=BF =E6=9D=A8=E8=99=8E =E9=87=8D=E5=BA=86=E4=BA=A4=E9=80=9A=E5=AD=A6=E9=99=A2= =E5=AD=A6=E6=8A=A5 9 (1), 16-23, 1990	1	1990
=E6=9C=80=E5=B0=8F=E4=BA=8C=E4=B9=98=E4=BC=B0=E8=AE=A1=E7=B2=BE= =E5=BA=A6=E5=8F=8A=E5=85=B6=E7=95=8C=E9=99=90 =E6= =9D=A8=E8=99=8E =E5=BA=94=E7=94=A8=E6=95=B0=E5= =AD=A6=E5=92=8C=E5=8A=9B=E5=AD=A6 11 (11), 1019-1025= , 1990	1	1990
Kantorovich-type inequalities and the measures o= f inefficiency of the glse S Wang, H Yang Acta Mathematicae Applicatae Sinica 5 (4), 372-381, 1989	11	1989
=E9=9B=86=E5=80=BC=E9=9D= =9E=E6=89=A9=E5=BC=A0=E7=AE=97=E5=AD=90=E7=9A=84=E9=9A=8F=E6=9C=BA=E4=B8=8D= =E5=8A=A8=E7=82=B9=E5=AE=9A=E7=90=86 =E6=9D=A8=E8= =99=8E =E9=87=8D=E5=BA=86=E4=BA=A4=E9=80=9A=E5= =A4=A7=E5=AD=A6=E5=AD=A6=E6=8A=A5 (=E8=87=AA=E7=84=B6=E7=A7=91=E5=AD=A6=E7= =89=88) 8 (3), 77, 1989		1989
=E5=88=A9=E7=94=A8=E6=88=90=E7=BB=A9=E6=A1=A3=E6=A1= =88=E5=AE=9E=E6=96=BD=E6=95=99=E5=8A=A1=E7=9B=AE=E6=A0=87=E7=B3=BB=E7=BB=9F= =E7=AE=A1=E7=90=86 =E8=99=8E=E6=9B=B9 =E8=93=93= =E3=80=81=E6=9D=A8 =E9=AB=98=E7=AD=89=E6=95=99= =E8=82=B2=E7=A0=94=E7=A9=B6 1 (1), 59-63, 1989		1989
=E5=8D=95=E5=8F=82=E6=95=B0=E4=B8= =BB=E6=88=90=E5=88=86=E5=9B=9E=E5=BD=92=E4=BC=B0=E8=AE=A1 =E6=9D=A8=E8=99=8E =E9=AB=98=E6=A0=A1= =E5=BA=94=E7=94=A8=E6=95=B0=E5=AD=A6=E5=AD=A6=E6=8A=A5: A =E8=BE=91 4 (1), = 74-80, 1989	<= a href=3D"https://scholar.google.com/scholar?oi=3Dbibs&hl=3Dzh-CN&c= ites=3D2239486012663498586" class=3D"gsc_a_ac gs_ibl">16	1989
=E5=9B=9E=E5=BD=92= =E8=AF=8A=E6=96=AD=E4=B8=AD=E6=95=B0=E6=8D=AE=E9=9B=86=E5=AF=B9=E5=90=84=E7= =82=B9=E6=8B=9F=E5=90=88=E5=80=BC=E7=9A=84=E5=BD=B1=E5=93=8D=E5=88=86=E5=B8= =83 =E6=9D=A8=E8=99=8E =E9=87=8D=E5=BA=86=E4=BA=A4=E9=80=9A=E5=AD=A6=E9=99=A2=E5=AD=A6=E6=8A=A5 = 8 (4), 44-52, 1989	2
Gauss-Markov= =E6=A8=A1=E5=9E=8B=E5=AD=98=E5=9C=A8=E5=A4=8D=E5=85=B1=E7=BA=BF=E6=80=A7= =E6=97=B6=E7=9A=84=E4=B8=80=E8=88=AC=E4=B8=BB=E6=88=90=E5=88=86=E5=9B=9E=E5= =BD=92 =E6=9D=A8=E8=99=8E =E9=87=8D=E5=BA=86=E4=BA=A4=E9=80=9A=E5=A4=A7=E5=AD=A6=E5=AD=A6=E6=8A= =A5 (=E8=87=AA=E7=84=B6=E7=A7=91=E5=AD=A6=E7=89=88) 7 (3), 109, 1988	4	1988
Kantorovich =E4=B8=8D=E7=AD=89=E5=BC= =8F=E7=9A=84=E5=BB=B6=E6=8B=93=E4=B8=8E=E5=9D=87=E6=96=B9=E8=AF=AF=E5=B7=AE= =E6=AF=94=E6=95=88=E7=8E=87 =E6=9D=A8=E8=99=8E =E5=BA=94=E7=94=A8=E6=95=B0=E5=AD=A6 4, 85-90, 1988	19	1988
Pages\| Received 27 May. 2024,= Accepted 18 Jan. 2025, Published online: 16 Feb. 2025 H Ming, H Yang, X Xia

=E7=B3=BB=E7=BB=9F=E7= =9B=AE=E5=89=8D=E6=97=A0=E6=B3=95=E6=89=A7=E8=A1=8C=E6=AD=A4=E6=93=8D=E4=BD= =9C=EF=BC=8C=E8=AF=B7=E7=A8=8D=E5=90=8E=E5=86=8D=E8=AF=95=E3=80=82

=E6=96=87=E7=AB=A0 1=E2=80=933= 25

=E9=9A=90=E7=A7=81= =E6=9D=83 = =E6=9D=A1=E6=AC=BE =E5=B8=AE=E5=8A=A9

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.gs_btn_act:focus:not(:active) { box-shadow: rgba(255, 2= 55, 255, 0.5) 0px 0px 0px 1px inset; } button.gs_btn_cre:active { background-color: rgb(176, 40, 26); } button.gs_btn_hov_nobg:hover, button.gs_btn_hov_nobg:active { border: none;= background: transparent; } button:disabled, button:disabled:hover, button:disabled:active { cursor: de= fault; color: rgb(184, 184, 184); border-color: rgba(0, 0, 0, 0.05); backgr= ound-color: transparent; z-index: 0; } button.gs_btn_flat:disabled { color: rgb(184, 184, 184); border-color: tran= sparent; } button.gs_btn_act:disabled { color: rgb(255, 255, 255); background-color: r= gb(166, 200, 255); } button.gs_btn_cre:disabled { color: rgb(255, 255, 255); background-color: r= gb(232, 164, 155); } button.gs_btn_da:disabled { color: rgb(255, 255, 255); background-color: rg= b(127, 165, 226); } a.gs_in_ib.gs_dis { cursor: default; pointer-events: none; } a.gs_in_ib.gs_dis .gs_lbl { color: rgb(184, 184, 184); text-decoration: non= e; } .gs_ttp { position: absolute; top: 100%; right: 50%; z-index: 10; pointer-e= vents: none; visibility: hidden; opacity: 0; transition: visibility 0.13s, = opacity 0.13s ease-out; } button:hover .gs_ttp, button:focus .gs_ttp, a:hover .gs_ttp, a:focus .gs_tt= p { transition: visibility 0.3s, opacity 0.13s ease-in 0.3s; visibility: vi= sible; opacity: 1; } .gs_md_tb.gs_sel .gs_ttp { transition: none; visibility: hidden; } button.gs_btn_lrge.gs_btn_cir .gs_ttp { top: 75%; } .gs_ttp .gs_aro, .gs_ttp .gs_aru { position: absolute; top: -2px; right: -5= px; width: 0px; height: 0px; line-height: 0; font-size: 0px; border-right: = 5px solid transparent; border-bottom: 5px solid rgb(89, 89, 89); border-lef= t: 5px solid transparent; border-image: initial; border-top: none; z-index:= 1; } .gs_ttp .gs_aro { top: -3px; right: -6px; border-right-width: 6px; border-b= ottom-width: 6px; border-left-width: 6px; border-top: none; border-bottom-c= olor: white; } .gs_ttp .gs_txt { display: block; position: relative; top: 2px; right: -50%= ; padding: 4px 6px; background: rgb(89, 89, 89); color: white; font-size: 1= 3px; font-weight: normal; line-height: normal; white-space: nowrap; border:= 1px solid white; border-radius: 3px; box-shadow: rgba(0, 0, 0, 0.2) 0px 1p= x 4px inset; } .gs_press, .gs_in_se, .gs_tan { touch-action: none; } .gs_in_se .gs_lbl:not(:empty) { padding-right: 14px; } .gs_in_se .gs_icm { position: absolute; top: 50%; margin-top: -5.5px; right= : 0px; width: 7px; height: 11px; background-position: -21px -88px; opacity:= 0.55; } .gs_in_se:hover .gs_icm { opacity: 0.72; } .gs_in_se:active .gs_icm { opacity: 1; } .gs_in_se:disabled .gs_icm { opacity: 0.28; } .gs_el_ta .gs_btn_hta .gs_icm, .gs_el_ph .gs_btn_hph .gs_icm, .gs_el_ta .gs= _btn_mta .gs_icm, .gs_el_ph .gs_btn_mph .gs_icm, .gs_el_ta .gs_btn_cta .gs_= icm, .gs_el_ph .gs_btn_cph .gs_icm { display: none; } .gs_btn_mnu .gs_icm { margin-top: -3.5px; height: 7px; background-position:= 0px -110px; } .gs_in_se.gs_btn_act .gs_icm, .gs_in_se.gs_btn_cre .gs_icm { margin-top: -3= .5px; height: 7px; background-position: -42px -44px; opacity: 1; } .gs_btn_act:disabled .gs_icm, .gs_btn_cre:disabled .gs_icm { opacity: 0.72;= } button.gs_btnG .gs_ico { width: 21px; height: 21px; background-position: -9= 2px -253px; } button .gs_bs { position: absolute; top: 50%; left: 50%; margin-top: -10px;= margin-left: -10px; box-sizing: border-box; width: 20px; height: 20px; bor= der-radius: 50%; border-width: 2px; border-style: solid; border-color: rgb(= 77, 144, 254) rgb(238, 238, 238) rgb(238, 238, 238); border-image: initial;= visibility: hidden; animation: 0.8s linear 0s infinite normal none running= gs_anm_spin; } button.gs_bsp .gs_bs { visibility: visible; transition: visibility 0.4s; } .gs_md_d { text-transform: none; white-space: nowrap; position: absolute; t= op: 0px; left: 0px; border: 1px solid rgba(0, 0, 0, 0.2); background: rgb(2= 55, 255, 255); box-shadow: rgba(0, 0, 0, 0.2) 0px 2px 4px; z-index: 1100; t= ext-align: left; visibility: hidden; max-height: 0px; margin-top: -1000px; = opacity: 0; transition: opacity 0.13s, visibility 0.13s, max-height 0.13s, = margin-top 0.13s; } .gs_md_d.gs_vis { visibility: visible; max-height: 10000px; margin-top: 0px= ; opacity: 1; transition: all; } .gs_el_tc .gs_md_d { transform-origin: 100% 0px; transform: scale(1, 0); tr= ansition: opacity 0.218s ease-out, transform 0.218s, visibility 0.218s, max= -height 0.218s, margin-top 0.218s; } .gs_el_ios .gs_md_d { backface-visibility: hidden; } .gs_el_tc .gs_md_d.gs_ttzi { transform-origin: 50% 50%; transform: scale(0,= 0); } .gs_el_tc .gs_md_d.gs_ttzr { transform: scale(0, 0); } .gs_el_tc .gs_md_d.gs_ttb { transform-origin: 50% 100%; } .gs_el_tc .gs_md_d.gs_vis { transform: scale(1, 1); transition: transform 0= .218s ease-out; } .gs_md_r { position: relative; display: inline-block; } .gs_md_rmb > .gs_md_d { top: 29px; } .gs_md_rmbl > .gs_md_d { top: 41px; } .gs_md_rmb_b > .gs_md_d { top: auto; bottom: 100%; overflow: hidden; } .gs_md_rmb_tr > .gs_md_d { top: 100%; left: auto; right: 0px; } .gs_md_ul { list-style-type: none; overflow-wrap: break-word; display: inli= ne-block; vertical-align: top; } .gs_md_ul.gs_md_ul_tb { display: block; } .gs_md_li, .gs_in_cb.gs_md_li, .gs_md_li:link, .gs_md_li:visited { display:= block; padding: 6px 44px 6px 16px; font-size: 13px; line-height: 16px; col= or: rgb(34, 34, 34); cursor: pointer; text-decoration: none; position: rela= tive; z-index: 0; } a.gs_md_li:hover .gs_lbl, a.gs_md_li:active .gs_lbl { text-decoration: none= ; } .gs_el_tc .gs_md_li { padding-top: 14px; padding-bottom: 10px; } .gs_md_li.gs_md_lix { font-size: 16px; line-height: 20px; padding: 12px 16p= x 8px; } .gs_md_li::before { content: ""; background-color: rgb(241, 241, 241); posi= tion: absolute; inset: 0px; opacity: 0; transition: opacity 0.13s; z-index:= -1; } .gs_md_li:hover::before, .gs_md_li:focus::before { opacity: 1; transition: = all; } a.gs_in_ib.gs_md_li .gs_lbl { color: rgb(34, 34, 34); } a.gs_in_ib.gs_md_li.gs_in_gray .gs_lbl { color: rgb(68, 68, 68); } .gs_md_li:active::before { background-color: rgb(221, 221, 221); } .gs_md_li.gs_sel, a.gs_in_ib.gs_md_li.gs_sel .gs_lbl { color: rgb(209, 72, = 54); } .gs_md_d:focus, .gs_md_li:focus { outline: none; } a.gs_md_lix .gs_lbl, a.gs_md_lix .gs_lbl:not(:empty) { padding: 0px 0px 0px= 40px; } a.gs_in_cb:link, a.gs_in_cb:visited, a.gs_in_cb:active, a.gs_in_cb:hover { = cursor: pointer; color: rgb(34, 34, 34); text-decoration: none; } .gs_in_cb, .gs_in_ra { position: relative; line-height: 16px; display: inli= ne-block; user-select: none; } .gs_in_cb.gs_md_li { padding: 6px 44px 6px 16px; } .gs_in_cb input, .gs_in_ra input { position: absolute; top: 1px; left: 1px;= width: 15px; height: 15px; margin: 0px; padding: 0px; opacity: 0; z-index:= 2; } .gs_in_ra input { top: 0px; left: 0px; } .gs_el_tc .gs_in_cb input { top: 9px; } .gs_el_tc .gs_in_ra input { top: 8px; } .gs_in_cb.gs_in_cbj input { top: 15px; left: 15px; } .gs_in_cb label, .gs_in_cb .gs_lbl, .gs_in_ra label { display: inline-block= ; padding-left: 21px; min-height: 16px; } .gs_in_ra_lrge { font-size: 15px; } .gs_in_cb label:empty::before, .gs_in_cb .gs_lbl:empty::before, .gs_in_ra l= abel:empty::before { content: "=E2=80=8B"; } .gs_el_tc .gs_in_cb label, .gs_el_tc .gs_in_cb .gs_lbl, .gs_el_tc .gs_in_ra= label { padding-top: 8px; padding-bottom: 5px; } .gs_in_cb.gs_in_cbj label, .gs_in_cb.gs_in_cbj .gs_lbl { padding: 13px 0px = 12px 41px; } .gs_in_cbb, .gs_in_cbb label, .gs_in_cbb .gs_lbl { display: block; } .gs_in_cb .gs_cbx, .gs_in_ra .gs_cbx { position: absolute; } .gs_in_cb .gs_cbx { top: 2px; left: 2px; width: 11px; height: 11px; border:= 1px solid rgb(198, 198, 198); border-radius: 1px; } .gs_md_li .gs_cbx { top: 8px; left: 18px; } .gs_el_tc .gs_in_cb .gs_cbx { top: 10px; } .gs_el_tc .gs_md_li .gs_cbx { top: 16px; } .gs_in_cb.gs_in_cbj .gs_cbx { top: 15px; left: 15px; } .gs_el_tc .gs_in_ra .gs_cbx { top: 8px; } .gs_in_ra .gs_cbx { top: 0px; left: 0px; border: 1px solid rgb(198, 198, 19= 8); width: 13px; height: 13px; border-radius: 7px; } .gs_in_cb:hover .gs_cbx, .gs_in_ra:hover .gs_cbx { border-color: rgb(102, 1= 02, 102); box-shadow: rgba(0, 0, 0, 0.1) 0px 1px 1px inset; } button.gs_in_cb:hover .gs_cbx { border-color: rgb(198, 198, 198); } .gs_in_cb :focus ~ label, .gs_in_ra :focus ~ label { outline: -webkit-focus= -ring-color auto; } .gs_pfcs .gs_in_cb :focus ~ label, .gs_pfcs .gs_in_ra :focus ~ label { outl= ine: none; } .gs_in_cb:active .gs_cbx, .gs_in_ra:active .gs_cbx, .gs_in_cb .gs_cbx:activ= e, .gs_in_ra .gs_cbx:active, .gs_in_cb :active ~ .gs_cbx, .gs_in_ra :active= ~ .gs_cbx { border-color: rgb(102, 102, 102); background-color: rgb(235, 2= 35, 235); } button.gs_in_cb:active .gs_cbx { border-color: rgb(166, 166, 166); } .gs_in_cb :disabled ~ .gs_cbx, .gs_in_ra :disabled ~ .gs_cbx, button.gs_in_= cb:disabled .gs_cbx { border-color: rgb(241, 241, 241); box-shadow: none; } .gs_in_cb :disabled ~ label, .gs_in_ra :disabled ~ label { color: rgb(184, = 184, 184); } .gs_in_cb.gs_err .gs_cbx { border-color: rgb(237, 162, 155); } .gs_in_cb .gs_chk, .gs_in_ra .gs_chk { position: absolute; z-index: 1; top:= -3px; left: -2px; width: 21px; height: 21px; } .gs_md_li .gs_chk { top: 3px; left: 14px; } .gs_el_tc .gs_in_cb .gs_chk { top: 5px; } .gs_el_tc .gs_md_li .gs_chk { top: 11px; } .gs_in_cb.gs_in_cbj .gs_chk { top: 10px; left: 11px; } .gs_in_ra .gs_chk { top: 4px; left: 4px; width: 7px; height: 7px; border-ra= dius: 4px; } .gs_el_tc .gs_in_ra .gs_chk { top: 12px; } .gs_in_cb input:checked ~ .gs_chk, .gs_in_cb.gs_sel .gs_chk { background: u= rl("/intl/zh-CN/scholar/images/1x/sprite_20161020.png") -69px -67px no-repe= at; opacity: 0.62; } .gs_in_ra input:checked ~ .gs_chk { background-color: rgb(102, 102, 102); } .gs_in_cb.gs_par .gs_chk { background: url("/intl/zh-CN/scholar/images/1x/s= prite_20161020.png") -21px -44px no-repeat; opacity: 0.55; } @media (-webkit-min-device-pixel-ratio: 1.5), (min-resolution: 144dpi) { .gs_in_cb input:checked ~ .gs_chk, .gs_in_cb.gs_sel .gs_chk, .gs_in_cb.gs= _par .gs_chk { background-image: url("/intl/zh-CN/scholar/images/2x/sprite_= 20161020.png"); background-size: 169px; } } .gs_in_cb input:checked:disabled ~ .gs_chk { opacity: 0.22; } .gs_in_ra input:checked:disabled ~ .gs_chk { background-color: rgb(241, 241= , 241); } .gs_ico_x { background-position: -113px -22px; opacity: 0.55; } .gs_ico_x:hover { opacity: 0.72; } .gs_ico_x:active { opacity: 1; } .gs_ico_X { background-position: -71px 0px; opacity: 0.55; } .gs_ico_X:hover { opacity: 0.72; } .gs_ico_X:active { opacity: 1; } .gs_btnX .gs_ico { background-position: -71px 0px; } .gs_el_tc .gs_ico_Xt { background-origin: content-box; background-clip: con= tent-box; padding: 10px 6px 10px 14px; } .gs_ico_P { background-position: 0px 0px; opacity: 0.55; } .gs_ico_P:hover { opacity: 0.72; } .gs_ico_P:active { opacity: 1; } .gs_btnPLS .gs_ico { background-position: 0px 0px; } .gs_btnP .gs_ico { background-position: -21px 0px; } .gs_btnC .gs_ico { background-position: 0px -66px; } .gs_btnL .gs_ico { background-position: -92px -44px; } .gs_ico_LB { background-position: -50px -44px; height: 16px; } .gs_btnJ .gs_ico { background-position: -92px -22px; } .gs_btnM .gs_ico { background-position: -92px 0px; } .gs_btnMW .gs_ico { background-position: -21px -22px; } .gs_btnSB .gs_ico { background-position: 0px -44px; } .gs_btnTSB .gs_ico { background-position: -115px -253px; } .gs_btnPL .gs_ico { background-position: -148px -66px; } .gs_btnPR .gs_ico { background-position: -21px -66px; } .gs_btnPLW .gs_ico { background-position: 0px -230px; } .gs_btnPRW .gs_ico { background-position: -23px -230px; } .gs_btnZI .gs_ico { background-position: -148px -22px; } .gs_btnZO .gs_ico { background-position: -127px -44px; } .gs_btnDE .gs_ico { background-position: -134px 0px; } .gs_btnFI .gs_ico { background-position: -50px -66px; } .gs_btnAD .gs_ico { background-position: -141px -88px; opacity: 0.55; } .gs_btnAD:hover .gs_ico { opacity: 0.72; } .gs_btnAD:active .gs_ico, .gs_btnAD .gs_ico:active, .gs_btnAD :active ~ .gs= _ico { opacity: 1; } .gs_btnBA .gs_ico { background-position: -50px -22px; } .gs_btnADD .gs_ico { background-position: -92px -66px; } .gs_btnMRG .gs_ico { background-position: -113px 0px; } .gs_btnLBL .gs_ico { background-position: 0px -161px; } .gs_btnCNCL .gs_ico { background-position: -71px 0px; } .gs_btnDWL .gs_ico { background-position: -28px -88px; } .gs_btnMNU .gs_ico { background-position: 0px -88px; } .gs_btnMNT .gs_ico { background-position: -46px -161px; } .gs_btnALT .gs_ico { background-position: -92px -161px; } .gs_btnART .gs_ico { background-position: -115px -161px; } .gs_btnGSL .gs_ico { background-position: -69px -161px; } .gs_btnCLS .gs_ico { background-position: -138px -161px; } .gs_btnXBLU .gs_ico { background-position: -138px -253px; } .gs_btnSSB .gs_ico { background-position: 0px -276px; } .gs_btnSSW .gs_ico { background-position: -23px -276px; } .gs_btnFLT .gs_ico { background-position: 0px -184px; } .gs_btnXT .gs_ico { background-position: -46px -184px; } .gs_btnPD .gs_ico { background-position: -69px -184px; } .gs_btnPU .gs_ico { background-position: -92px -276px; } .gs_btnCP .gs_ico { background-position: -92px -184px; } .gs_btnTP .gs_ico { background-position: -138px -184px; } .gs_btnML .gs_ico { background-position: -115px -276px; } .gs_btnCHK .gs_ico { background-position: -71px -66px; } .gs_btnDNB .gs_ico { background-position: -115px -230px; } .gs_btnDNW .gs_ico { background-position: 0px -207px; } .gs_btnACA .gs_ico { background-position: -23px -207px; } .gs_btnAPT .gs_ico { background-position: -46px -207px; } .gs_btnAPTW .gs_ico { background-position: -92px -230px; } .gs_btnAFL .gs_ico { background-position: -69px -207px; } .gs_btnAN .gs_ico { background-position: -46px -276px; } .gs_btnAI .gs_ico { background-position: -69px -276px; } .gs_btnPBL .gs_ico { background-position: -92px -207px; } .gs_btnUCT .gs_ico { background-position: -115px -207px; } .gs_btnVRF .gs_ico { background-position: -138px -207px; } .gs_btnLSI .gs_ico { background-position: -46px -230px; } .gs_btnLSG .gs_ico { background-position: -69px -230px; } .gs_btnMOR .gs_ico { background-position: -23px -253px; } .gs_btnADV .gs_ico { background-position: -46px -253px; } .gs_btnPRO .gs_ico { background-position: -69px -253px; } .gs_btnLAB .gs_ico, .gs_ico_LAB { background-position: -69px -345px; } .gs_ico_LAB { width: 21px; height: 21px; } .gs_ico_star { background-position: -71px -44px; width: 13px; height: 13px;= } .gs_btnPLSW .gs_ico { background-position: -138px -230px; } .gs_btnPDF .gs_ico { background-position: 0px -253px; } .gs_btnS .gs_ico { background-position: -138px -276px; } .gs_btnUNS .gs_ico { background-position: 0px -299px; } .gs_btnMORR .gs_ico { background-position: -23px -299px; } .gs_btnTW .gs_ico { background-position: -46px -299px; } .gs_btnIN .gs_ico { background-position: -69px -299px; } .gs_btnFB .gs_ico { background-position: -92px -299px; } .gs_btnET .gs_ico { background-position: -115px -299px; } .gs_btnARC .gs_ico { background-position: -138px -299px; } .gs_btnOL .gs_ico { background-position: 0px -322px; } .gs_btnFA .gs_ico { background-position: -23px -322px; } .gs_btnFAD .gs_ico { background-position: -46px -322px; } .gs_btnHP .gs_ico { background-position: -69px -322px; } .gs_btnPLM .gs_ico { background-position: -92px -322px; } .gs_btnPRM .gs_ico { background-position: -115px -322px; } .gs_btnRN .gs_ico { background-position: -138px -322px; } .gs_btnVF .gs_ico { background-position: 0px -345px; } .gs_btnVP .gs_ico { background-position: -23px -345px; } .gs_btnSRT .gs_ico { background-position: -46px -345px; } #gs_md_s.gs_hdr_drs { transition: opacity 0.15s, visibility 0.15s; } #gs_md_s.gs_hdr_drs.gs_vis { transition: opacity 0.15s, visibility; } .gs_el_tc #gs_md_s.gs_hdr_drs { transition: opacity 0.218s, visibility 0.21= 8s; } .gs_el_tc #gs_md_s.gs_hdr_drs.gs_vis { transition: opacity 0.218s, visibili= ty; } #gs_hdr_drw { position: fixed; top: 0px; left: 0px; height: 100%; z-index: = 1200; visibility: hidden; overflow: auto; width: 236px; background-color: r= gb(255, 255, 255); box-shadow: rgba(0, 0, 0, 0.15) 2px 2px 4px; outline: no= ne; transform: translate(-100%, 0px); transition: transform 0.15s ease-in-o= ut, visibility 0.15s; } #gs_hdr_drw.gs_vis { visibility: visible; transform: translate(0px, 0px); t= ransition: transform 0.15s ease-in-out, visibility; } .gs_el_tc #gs_hdr_drw { transition: transform 0.3s cubic-bezier(0.4, 0, 0.6= , 1), visibility 0.3s; } .gs_el_tc #gs_hdr_drw.gs_vis { transition: transform 0.225s cubic-bezier(0,= 0, 0.2, 1), visibility; } #gs_top #gs_hdr_drw.gs_abt, #gs_top #gs_md_s.gs_abt { transition: none; } #gs_hdr_drw_in { position: relative; box-sizing: border-box; min-height: 10= 0%; padding: 0px 0px 8px; } .gs_el_ta #gs_hdr_drw_in, .gs_el_ph #gs_hdr_drw_in { padding: 0px 0px 65px;= } #gs_hdr_drw_top { position: relative; height: 63px; border-bottom: 1px soli= d rgb(229, 229, 229); margin-bottom: 8px; } .gs_el_ta #gs_hdr_drw_top, .gs_el_ph #gs_hdr_drw_top { height: 57px; } #gs_hdr_drw_mnu, #gs_hdr_drw_lgo { position: absolute; top: 0px; height: 10= 0%; } #gs_hdr_drw_mnu { left: 0px; width: 55px; } #gs_hdr_drw_lgo { left: 56px; } .gs_hdr_drw_sec::before { display: block; content: " "; height: 0px; border= -bottom: 1px solid rgb(229, 229, 229); margin: 8px 0px; } .gs_hdr_drw_sec:first-child::before { display: none; } #gs_hdr_drw_bot { display: none; } .gs_el_ta #gs_hdr_drw_bot, .gs_el_ph #gs_hdr_drw_bot { display: block; posi= tion: absolute; left: 0px; bottom: 0px; width: 100%; height: 65px; } #gs_hdr_drw_bot .gs_md_li::before { opacity: 0; } #gs_hdr_drw_bot .gs_hdr_pp { display: block; position: absolute; bottom: 14= px; left: 15px; pointer-events: none; } #gs_hdr_drw_bot .gs_lbl { display: block; white-space: nowrap; overflow: hi= dden; text-overflow: ellipsis; } #gs_hdr { position: relative; height: 63px; background-color: rgb(245, 245,= 245); border-bottom: 1px solid rgb(229, 229, 229); display: flex; } .gs_el_ta #gs_hdr, .gs_el_ph #gs_hdr { height: 57px; } #gs_hdr_mnu, #gs_hdr_bck, #gs_hdr_lgo, #gs_hdr_llgo, #gs_hdr_lgt, #gs_hdr_m= d, #gs_hdr_sre, #gs_hdr_hmc, #gs_hdr_act { display: inline-block; vertical-= align: top; position: relative; height: 100%; flex: 0 0 auto; } #gs_hdr_md { flex: 1 1 auto; } #gs_hdr .gs_hdr_mbo, #gs_hdr .gs_hdr_mbo, .gs_el_ta #gs_hdr .gs_hdr_dso, .g= s_el_ph #gs_hdr .gs_hdr_dso { display: none; } .gs_el_ta #gs_hdr .gs_hdr_mbo, .gs_el_ph #gs_hdr .gs_hdr_mbo { display: inl= ine-block; } #gs_hdr_mnu, #gs_hdr_bck, #gs_hdr_sre { width: 55px; margin-right: 1px; } #gs_hdr_lgo, #gs_hdr_drw_lgo { width: 158px; background: url("/intl/zh-CN/s= cholar/images/1x/scholar_logo_24dp.png") 0% 50% / 158px no-repeat; } @media (-webkit-min-device-pixel-ratio: 1.5), (min-resolution: 144dpi) { #gs_hdr_lgo, #gs_hdr_drw_lgo { background-image: url("/intl/zh-CN/scholar= /images/2x/scholar_logo_24dp.png"); } } #gs_hdr_lgo { margin-right: 30px; } .gs_el_ph #gs_hdr_lgo { margin-right: 0px; } #gs_hdr_lgt { min-width: 172px; margin-right: 16px; } #gs_hdr_lgt:empty { display: none; } #gs_hdr_md { margin-right: 16px; min-width: 1px; } #gs_hdr_lgt, #gs_hdr_md h1 { padding: 19px 0px 0px; white-space: nowrap; ov= erflow: hidden; text-overflow: ellipsis; font-size: 20px; line-height: 25px= ; font-weight: normal; color: rgb(102, 102, 102); max-width: 100%; text-ali= gn: left; } .gs_el_ta #gs_hdr_md h1, .gs_el_ph #gs_hdr_md h1 { padding: 16px 0px 0px; } #gs_hdr_srch { padding: 14px 0px 0px; max-width: 600px; } .gs_el_ta #gs_hdr_srch, .gs_el_ph #gs_hdr_srch { padding: 10px 0px 0px; max= -width: none; } #gs_hdr_frm { position: relative; padding-right: 39px; } #gs_hdr_tsi { height: 38px; border-radius: 2px 0px 0px 2px; } #gs_hdr_tsi::-ms-clear { display: none; } #gs_hdr_tsc { display: none; position: absolute; top: 3px; right: 41px; wid= th: 21px; height: 21px; padding: 6px 10px 7px; } .gs_in_acw[dir=3D"rtl"] ~ #gs_hdr_tsc { right: auto; left: 1px; } #gs_hdr_tsb { position: absolute; top: 0px; right: 0px; width: 40px; height= : 38px; border-radius: 0px 2px 2px 0px; } #gs_hdr_frm_ac { top: 37px; right: 40px; } .gs_el_ph #gs_hdr_frm_ac { right: 0px; } .gs_el_ph .gs_hdr_ifc #gs_hdr_mnu, .gs_el_ph .gs_hdr_ifc #gs_hdr_bck, .gs_h= dr_src #gs_hdr_srch, .gs_hdr_src #gs_hdr_lgt, .gs_hdr_srx #gs_hdr_sre, .gs_= hdr_srx #gs_hdr_md h1, .gs_hdr_srx #gs_hdr_md h1.gs_hdr_mbo, .gs_hdr_srx #g= s_hdr_md h1.gs_hdr_dso, .gs_el_ta .gs_hdr_srx #gs_hdr_lgo, .gs_el_ph .gs_hd= r_srx #gs_hdr_lgo, .gs_el_ph .gs_hdr_srx #gs_hdr_mnu, .gs_el_ph .gs_hdr_srx= #gs_hdr_bck { display: none; } .gs_el_ph .gs_hdr_ifc #gs_hdr_md, .gs_el_ph .gs_hdr_srx #gs_hdr_md { margin= -left: 16px; } .gs_el_tc .gs_hdr_tsc #gs_hdr_tsi[dir=3D"ltr"] { padding-right: 41px; } .gs_el_tc .gs_hdr_tsc #gs_hdr_tsi[dir=3D"rtl"] { padding-left: 41px; } .gs_el_tc .gs_hdr_tsc .gs_in_acw ~ #gs_hdr_tsc { display: block; } #gs_hdr_hmc { margin-right: 16px; } #gs_hdr_hmc .gs_md_rmb { height: 100%; display: flex; align-items: center; = } #gs_hdr_act { min-width: 64px; max-width: 200px; text-align: right; float: = right; } .gs_el_ta #gs_hdr_act, .gs_el_ph #gs_hdr_act { display: none; } #gs_hdr_act_i, #gs_hdr_act_s { display: inline-block; padding: 23px 24px 23= px 16px; max-width: 100%; box-sizing: border-box; font-size: 13px; line-hei= ght: 17px; white-space: nowrap; overflow: hidden; text-overflow: ellipsis; = color: rgb(68, 68, 68); } #gs_hdr_act_s { font-size: 15px; } .gs_el_sm #gs_hdr_act_i, .gs_el_sm #gs_hdr_act_s { padding: 23px 16px; } .gs_el_ta #gs_hdr_act_i, .gs_el_ta #gs_hdr_act_s, .gs_el_ph #gs_hdr_act_i, = .gs_el_ph #gs_hdr_act_s { padding: 20px 16px; } #gs_hdr_act_i:active, #gs_hdr_act_s:active { color: rgb(209, 72, 54); } #gs_hdr_act_i, .gs_el_sm #gs_hdr_act_i { padding-top: 15px; padding-bottom:= 16px; } .gs_el_ta #gs_hdr_act_i, .gs_el_ph #gs_hdr_act_i { padding-top: 12px; paddi= ng-bottom: 13px; } #gs_hdr_act_i .gs_hdr_pp { vertical-align: top; } #gs_hdr_act_d { top: 63px; left: auto; right: 24px; min-width: 288px; max-w= idth: 400px; } .gs_el_sm #gs_hdr_act_d { right: 16px; } .gs_el_ta #gs_hdr_act_d { top: 57px; } .gs_el_ph #gs_hdr_act_d { top: 57px; min-width: 280px; max-width: 90vw; } #gs_hdr_act_aw, #gs_hdr_act_ap, .gs_hdr_act_am, #gs_hdr_act_ab { display: b= lock; padding: 10px 20px; overflow-wrap: break-word; white-space: normal; } #gs_hdr_act_aw { background-color: rgb(254, 249, 219); font-size: 11px; } #gs_hdr_act_ap, .gs_hdr_act_am { border-bottom: 1px solid rgb(204, 204, 204= ); } #gs_hdr_act_ap { padding: 20px; } .gs_el_ph #gs_hdr_act_ap { padding: 10px; } #gs_hdr_act_apb { margin-top: 12px; } #gs_hdr_act_aa:link, #gs_hdr_act_aa:visited { float: right; margin-left: 8p= x; color: rgb(26, 13, 171); } #gs_hdr_act_aa:active { color: rgb(209, 72, 54); } .gs_hdr_act_am:link, .gs_hdr_act_am:visited { color: rgb(34, 34, 34); text-= decoration: none; background: rgb(251, 251, 251); } .gs_hdr_act_am:hover, .gs_hdr_act_am:focus { background: rgb(241, 241, 241)= ; } .gs_hdr_act_am:active { background: rgb(238, 238, 238); } #gs_hdr_act_ab { background: rgb(251, 251, 251); padding: 10px 0px; display= : table; width: 100%; white-space: nowrap; } #gs_hdr_act_aba, #gs_hdr_act_abs { display: table-cell; padding: 0px 20px; = } #gs_hdr_act_abs { text-align: right; } .gs_el_ph #gs_hdr_act_aba, .gs_el_ph #gs_hdr_act_abs { display: block; padd= ing: 10px; text-align: center; } .gs_el_ph #gs_hdr_act_aba button, .gs_el_ph #gs_hdr_act_abs button { width:= 100%; } #gs_hdr_act_a1, #gs_hdr_act_a2 { position: absolute; top: -9px; right: 7.5p= x; width: 0px; height: 0px; z-index: 1; border-right: 8.5px solid transpare= nt; border-bottom: 8.5px solid rgba(0, 0, 0, 0.2); border-left: 8.5px solid= transparent; border-image: initial; border-top: none; } #gs_hdr_act_a2 { top: -8px; border-bottom-color: rgb(255, 255, 255); } .gs_hdr_act_mw #gs_hdr_act_a2 { border-bottom-color: rgb(254, 249, 219); } .gs_hdr_pp { border-radius: 50%; overflow: hidden; } #gs_hdr_act_ap .gs_hdr_pp, .gs_hdr_act_am .gs_hdr_pp { float: left; } #gs_hdr_act_ap .gs_hdr_pm { margin-left: 116px; } .gs_hdr_act_am .gs_hdr_pm { margin: 6px 0px 0px 58px; } #gs_ab { position: relative; height: 41px; border-bottom: 1px solid rgb(229= , 229, 229); display: flex; white-space: nowrap; background-color: rgb(255,= 255, 255); z-index: 1000; } .gs_el_ta #gs_ab.gs_nta, .gs_el_ph #gs_ab.gs_nph { display: none; } .gs_sth_vis #gs_ab { position: fixed; } #gs_ab_ico, #gs_ab_ttl, #gs_ab_md, #gs_ab_btns { display: inline-block; ver= tical-align: top; position: relative; height: 100%; flex: 0 0 auto; } .gs_el_ph #gs_ab_md { display: block; } #gs_ab_ico { width: 55px; margin-right: 1px; } #gs_ab_ico .gs_ico { position: absolute; top: 50%; left: 50%; margin: -10.5= px 0px 0px -10.5px; } #gs_ab_ttl { min-width: 180px; padding-right: 8px; } .gs_el_sm #gs_ab_ttl { min-width: 68px; } .gs_el_ta #gs_ab_ttl, .gs_el_ph #gs_ab_ttl { min-width: 0px; } #gs_ab_ttl, #gs_ab_ttll { font-size: 18px; color: rgb(102, 102, 102); text-= transform: none; } .gs_el_sm #gs_ab_ttl, .gs_el_sm #gs_ab_ttll { font-size: 16px; } #gs_ab_ttll { overflow: hidden; text-overflow: ellipsis; max-width: 200px; = } #gs_ab_md { flex: 1 0 auto; } .gs_ab_st #gs_ab_md { flex: 1 1 auto; font-size: 13px; line-height: 17px; p= adding: 0px 8px; color: rgb(153, 153, 153); overflow: hidden; text-overflow= : ellipsis; } .gs_el_ph .gs_ab_st #gs_ab_md { visibility: hidden; padding: 0px; } #gs_ab_btns { margin-right: 8px; } .gs_el_sm #gs_ab_btns { margin-right: 0px; } .gs_el_ta #gs_ab_btns, .gs_el_ph #gs_ab_btns { margin-right: 4px; } #gs_ab_ttl::before, #gs_ab_md::before, #gs_ab_btns::before { content: ""; d= isplay: inline-block; width: 0px; height: 100%; vertical-align: middle; } #gs_ab_md > button, #gs_ab_btns > button, #gs_ab_md > .gs_in_ib, #gs_ab_btn= s > .gs_in_ib, #gs_ab_md > .gs_md_r, #gs_ab_btns > .gs_md_r, #gs_ab .gs_ab_= mdw, #gs_ab .gs_ab_btw { margin: 0px 8px; vertical-align: middle; } #gs_ab .gs_ab_mdw, .gs_ab_btw { display: inline-block; margin: 0px; } #gs_ab_btns > .gs_in_ib { margin: 0px 16px 0px 8px; } #gs_ab .gs_ab_btw { margin: 0px 12px 0px 16px; } .gs_el_ta .gs_ab_sel #gs_ab_ico, .gs_el_ph .gs_ab_sel #gs_ab_ico, .gs_el_ta= .gs_ab_sel #gs_ab_ttl, .gs_el_ph .gs_ab_sel #gs_ab_ttl, .gs_el_ta .gs_ab_s= el #gs_ab_btns, .gs_el_ph .gs_ab_sel #gs_ab_btns { display: none; } #gs_bdy { display: table; table-layout: fixed; width: 100%; } #gs_bdy_sb { vertical-align: top; width: 236px; overflow-wrap: break-word; = display: none; } .gs_el_sm #gs_bdy_sb { } .gs_el_ta #gs_bdy_sb, .gs_el_ph #gs_bdy_sb { } .gs_bdy_sb_sec { margin: 0px 40px 0px 56px; } .gs_bdy_sb_sec::before { display: block; content: " "; height: 0px; margin:= 13px 0px; border-top: 1px solid rgb(238, 238, 238); } .gs_bdy_sb_sec:first-child::before { margin: 21px 0px 0px; border: none; } #gs_bdy_sb ul { list-style-type: none; } .gs_bdy_sb_sec a:link, .gs_bdy_sb_sec a:visited { color: rgb(34, 34, 34); } .gs_bdy_sb_sec a:active { color: rgb(209, 72, 54); } .gs_bdy_sb_sel a:link, .gs_bdy_sb_sel a:visited { color: rgb(209, 72, 54); = text-decoration: none; } .gs_el_tc .gs_bdy_sb_sec li.gs_ind, .gs_el_tc .gs_bdy_sb_sec li.gs_ind a { = padding-top: 8px; padding-bottom: 5px; } .gs_el_tc .gs_bdy_sb_sec:first-child li.gs_ind:first-child { margin-top: -8= px; } #gs_bdy_sb .gs_ind, #gs_bdy_sb .gs_inw { margin-bottom: 6px; } .gs_el_tc #gs_bdy_sb .gs_ind, .gs_el_tc #gs_bdy_sb .gs_inw { margin-bottom:= 0px; } #gs_bdy_ccl { display: table-cell; vertical-align: top; padding: 0px 24px 0= px 16px; } .gs_el_sm #gs_bdy_ccl { padding: 0px 16px; } .gs_el_ta #gs_bdy_ccl, .gs_el_ph #gs_bdy_ccl { padding: 0px 16px; } .gs_el_ph #gs_bdy_ccl { padding: 0px; } #gs_ftr_sp { height: 62px; } .gs_el_sm #gs_ftr_sp { height: 57px; } #gs_ftr { position: absolute; bottom: 0px; left: 0px; width: 100%; white-sp= ace: nowrap; border-top: 1px solid rgb(228, 228, 228); background-color: rg= b(242, 242, 242); display: flex; } #gs_ftr.gs_pfix { position: fixed; } #gs_ftr_rt { box-sizing: border-box; max-width: 100%; overflow-x: auto; mar= gin-left: auto; padding: 0px 12px; } .gs_el_sm #gs_ftr_rt { padding: 0px 8px; } .gs_el_ph #gs_ftr_rt::after { content: " "; position: absolute; top: 0px; r= ight: 0px; width: 16px; height: 100%; background-image: linear-gradient(to = right, rgba(242, 242, 242, 0), rgb(242, 242, 242) 80%); } #gs_ftr_rt > a { display: inline-block; line-height: 16px; padding: 12px; w= hite-space: nowrap; } .gs_el_sm #gs_ftr_rt > a { padding: 12px 8px; } #gs_ftr_rt > a:link, #gs_ftr_rt > a:visited { color: rgb(102, 102, 102); } #gs_ftr_rt > a:active { color: rgb(209, 72, 54); } #gs_ftr_mnu { inset: auto 24px 48px auto; padding: 8px 0px; } .gs_el_sm #gs_ftr_mnu { right: 16px; } #gsc_a_t { width: 100%; table-layout: fixed; } #gsc_a_tr0, #gsc_a_trh { box-sizing: border-box; } #gsc_a_tr0 th.gsc_a_x, #gsc_a_tr0 th.gsc_a_t, #gsc_a_tr0 th.gsc_a_c, #gsc_a= _tr0 th.gsc_a_y { height: 0px; } #gsc_a_trh { z-index: 700; background-color: rgb(245, 245, 245); height: 42= px; } .gs_el_ta #gsc_a_trh, .gs_el_ph #gsc_a_trh, .gs_el_ta #gsc_a_t td, .gs_el_p= h #gsc_a_t td { background-color: rgb(255, 255, 255); border-bottom: 1px so= lid rgb(229, 229, 229); } #gsc_a_t th.gsc_a_x, #gsc_a_t th.gsc_a_t, #gsc_a_t th.gsc_a_c, #gsc_a_t th.= gsc_a_y { box-sizing: border-box; font-size: 15px; vertical-align: middle; = padding-top: 0px; padding-bottom: 0px; } #gsc_x_all { z-index: 1; } .gsc_a_x, .gsc_a_t, .gsc_a_c, .gsc_a_y, .gsc_a_e { font-weight: normal; pad= ding: 16px 16px 0px; vertical-align: top; text-align: right; } .gsc_a_c { padding: 16px 8px 0px; } .gs_el_sm .gsc_a_x, .gs_el_sm .gsc_a_t, .gs_el_sm .gsc_a_c { padding: 12px = 8px 0px; } .gs_el_ta .gsc_a_x, .gs_el_ta .gsc_a_t, .gs_el_ta .gsc_a_c, .gs_el_ph .gsc_= a_x, .gs_el_ph .gsc_a_t, .gs_el_ph .gsc_a_c { padding: 12px 8px; } .gs_el_sm .gsc_a_y { padding: 12px 8px 0px; } .gs_el_ta .gsc_a_y { padding-bottom: 12px; } .gsc_a_x { width: 41px; padding: 4px 0px 0px; } .gs_el_sm .gsc_a_x { padding: 0px; } .gsc_a_t { text-align: left; } .gs_el_ph .gsc_a_t { padding-left: 16px; } #gsc_a_ta { display: inline-block; vertical-align: middle; margin-right: 16= px; } .gs_el_ph #gsc_a_ta { display: none; } .gs_el_ph .gsc_a_c { padding-right: 16px; } th.gsc_a_c { width: 64px; white-space: nowrap; } .gsc_art_sel #gsc_a_ta, .gsc_art_sel #gsc_a_ca, .gsc_art_sel .gsc_a_h { dis= play: none; } .gsc_a_ac, .gsc_a_hc { margin-top: 3px; } th.gsc_a_y { width: 88px; white-space: nowrap; } .gs_el_sm th.gsc_a_y { width: 55px; } .gs_el_ph th.gsc_a_y, .gs_el_ph td.gsc_a_y { width: 0px; padding: 0px; } .gs_el_ph .gsc_a_h { display: none; } @media print { #gs_top th.gsc_a_y { width: 58pt; } #gs_top #gsc_a_tr0 { display: none; } #gs_top #gsc_a_trh { position: static; } } .gsc_a_e { padding: 16px; text-align: center; } .gsc_a_a { padding: 8px 0px; } .gsc_a_at { padding: 8px 0px; font-size: 16px; } a.gsc_a_acm { text-decoration: line-through; } a.gsc_a_acm:hover, a.gs_a_acm:active { text-decoration: underline; } .gsc_a_m { position: absolute; } .gs_el_ph .gsc_a_m { display: block; position: static; } .gsc_a_am { font-size: 24px; position: absolute; top: -18px; left: -2px; pa= dding: 8px 12px 4px 8px; } .gs_el_ph .gsc_a_am { display: inline-block; position: static; padding: 6px= 16px; margin-bottom: -6px; } #gsc_a_sp { visibility: hidden; } #gsc_a_sp.gs_vis { visibility: visible; padding: 16px 0px; height: 25px; bo= rder-bottom: 1px solid rgb(204, 204, 204); } #gsc_a_sp::after { display: block; height: 100%; content: " "; background: = url("/intl/zh-CN/scholar/images/spinner.gif") 50% 50% no-repeat; opacity: 0= ; } #gsc_a_sp.gs_vis::after { opacity: 1; transition: opacity 0.4s; } #gsc_a_err { display: none; padding: 28px 0px; } #gsc_a_err.gs_vis { display: block; } #gsc_md_iad { width: 800px; max-width: 94%; } .gs_el_ph #gsc_md_iad { width: 100%; max-width: 100%; } #gsc_md_iad .gs_md_prg { min-height: 400px; } .gs_el_ph #gsc_iads_res .gs_md_prg { margin: 0px 16px; } #gsc_iad_tart, .gsc_iad_tsel.gsc_iad_tart #gsc_iad_tart, .gsc_iad_tart #gsc= _iad_tgrp, .gsc_iad_tsel #gsc_iad_tgrp, #gsc_iad_tsel, #gsc_napb_hdr #gsc_i= ad_tart, #gsc_napb_hdr #gsc_iad_tgrp { display: none; } #gsc_iad_tgrp, .gsc_iad_tart #gsc_iad_tart, .gsc_iad_tsel #gsc_iad_tsel, #g= sc_napb_hdr #gsc_iad_tsel { display: inline-block; } #gsc_iad_t:not(.gsc_iad_tsel) #gsc_iad_tsel { pointer-events: none; color: = rgb(181, 181, 181); } .gs_el_ph #gsc_napb #gsc_iads_frm { margin: 0px 16px; } #gsc_iads_res { position: relative; margin: 16px 0px; min-height: 80px; bor= der-bottom: 1px solid rgb(229, 229, 229); } .gs_el_ph #gsc_md_iad #gsc_iads_res { margin: 8px -16px 16px; } .gs_el_ph #gsc_napb #gsc_iads_pp { margin-right: 16px; } #gsc_iads_btns { height: 38px; border-bottom: 1px solid rgb(229, 229, 229);= box-sizing: border-box; margin-top: 20px; } .gs_el_ph #gsc_iads_btns { margin-top: 16px; } a.gsc_iads_btn { display: inline-flex; align-items: center; box-sizing: bor= der-box; height: 38px; padding: 0px 12px; color: rgb(119, 119, 119); } .gs_el_ph .gsc_iads_btn { padding: 0px 16px; } a.gsc_iads_btn.gs_sel { border-bottom: 2px solid rgb(77, 144, 254); color: = rgb(34, 34, 34); } a.gsc_iads_btn.gs_dis { cursor: default; pointer-events: none; color: rgb(1= 84, 184, 184); } .gsc_iads_btn:hover { text-decoration: none; color: rgb(68, 68, 68); backgr= ound-color: rgb(245, 245, 245); } .gsc_iads_btn:active { background-color: rgb(229, 229, 229); } a.gsc_iads_btn .gs_ico { opacity: 0.55; } a.gsc_iads_btn:hover .gs_ico { opacity: 0.72; } a.gsc_iads_btn.gs_sel .gs_ico, a.gsc_iads_btn:active .gs_ico { opacity: 1; = } a.gsc_iads_btn.gs_dis .gs_ico { opacity: 0.28; } .gsc_iads_btn_lbl { padding-left: 10px; margin-bottom: -2px; } #gsc_iadb_hdr { display: table; table-layout: fixed; width: 100%; } #gsc_iadb_hdr_cb, #gsc_iadb_hdr_instr { display: table-cell; vertical-align= : middle; height: 41px; } #gsc_iadb_hdr_cb { width: 41px; } #gsc_iadb_hdr_cb:empty { width: 0px; } .gs_el_ph #gsc_iadb_hdr_cb:empty { width: 16px; } #gsc_iadb_hdr_instr { font-size: 16px; } .gs_el_ph #gsc_iadb_hdr_instr { padding-right: 16px; } #gsc_iadb_hdr_cb:empty, .gs_el_ph #gsc_iadb_hdr_cb:empty, #gsc_iadb_hdr_ins= tr:empty { height: 0px; } .gsc_oic { position: relative; } .gsc_oic_cb { font-weight: normal; border-top: 1px solid rgb(229, 229, 229)= ; border-bottom: 1px solid rgb(229, 229, 229); background-color: rgb(252, 2= 52, 252); padding-right: 16px; } .gsc_oict { display: block; overflow: hidden; } .gsc_oict_name { display: block; font-size: 16px; line-height: 20px; overfl= ow-wrap: break-word; } .gsc_oict_inf { display: block; float: right; margin-left: 16px; white-spac= e: nowrap; } .gsc_oict_all, .gsc_oict_prf { font-size: 16px; line-height: 20px; } .gsc_oict_all[data-a] { color: rgb(26, 13, 171); cursor: pointer; } .gsc_oict_all[data-a]:hover { text-decoration: underline; } .gsc_oict_all[data-a]:active { color: rgb(209, 72, 54); } .gsc_oict_prf { padding-left: 8px; margin-left: 8px; border-left: 1px solid= rgb(224, 224, 224); } .gsc_oict_prf:empty { display: none; } .gs_el_ph .gsc_oict_all, .gs_el_ph .gsc_oict_prf { float: right; clear: bot= h; margin: 0px; padding: 0px; border: none; } .gs_el_ph .gsc_oict_prf { margin-top: 2px; } .gsc_oic_res { margin: 8px 0px 12px 41px; } .gs_el_ph .gsc_oic_res { margin-right: 16px; } .gsc_oic_res h4 { font-size: 13px; font-weight: normal; } .gsc_oic_res b { color: rgb(221, 75, 57); font-weight: normal; } .gsc_oic_dis .gsc_oic_name, .gsc_oic_dis .gsc_oic_all, .gsc_oic_dis .gsc_oi= c_prf { color: rgb(119, 119, 119); } .gsc_oic_dis .gsc_oict_all[data-a] { color: rgb(26, 13, 171); opacity: 0.66= ; } .gsc_oic_dis .gsc_oic_res { opacity: 0.5; } .gsc_iadb_art { border-top: 1px solid rgb(229, 229, 229); overflow: hidden;= } .gsc_iadb_art_cb { float: left; } .gsc_iadb_art_added { float: right; margin: 12px; font-size: 13px; color: r= gb(119, 119, 119); } .gs_el_ph .gsc_iadb_art_added { display: block; float: none; text-align: ri= ght; margin: 8px 16px; } .gsc_iadb_art_added:empty { display: none; } .gsc_iadb_art_body { margin: 12px 0px 12px 41px; } .gs_el_ph .gsc_iadb_art_body { margin: 12px 16px 12px 41px; } .gsc_iadb_art_body h3 { font-size: 13px; font-weight: normal; } .gsc_iadb_art_dis .gsc_iadb_art_body { opacity: 0.5; } #gsc_md_mopt, #gsc_md_cbyd, #gsc_md_cbym { width: 600px; } .gs_el_ta #gsc_md_mopt, .gs_el_ta #gsc_md_cbyd, .gs_el_ta #gsc_md_cbym { wi= dth: 500px; } .gs_el_ph #gsc_md_mopt, .gs_el_ph #gsc_md_cbyd, .gs_el_ph #gsc_md_cbym { wi= dth: 100%; } .gsc_mob_art { vertical-align: top; padding: 8px 0px; } .gs_el_tc .gsc_mob_art > .gs_in_ra { margin-top: -8px; } .gsc_mob_cby { vertical-align: top; text-align: right; padding: 8px 12px; p= osition: relative; } .gsc_mob_ttl, .gsc_mob_pub { display: block; } .gsc_mob_pub { color: rgb(102, 102, 102); } .gsc_mob_cbym { text-decoration: line-through; } .gsc_mob_cbm { font-size: 24px; position: absolute; padding: 4px 0px 0px 4p= x; line-height: 16px; } .gs_fsvg line { stroke: rgb(34, 34, 34); } a:link .gs_fsvg { fill: rgb(26, 13, 171); } a:link .gs_fsvg line { stroke: rgb(26, 13, 171); } a:visited .gs_fsvg { fill: rgb(102, 0, 153); } a:visited .gs_fsvg line { stroke: rgb(102, 0, 153); } a:active .gs_fsvg { fill: rgb(209, 72, 54); } a:active .gs_fsvg line { stroke: rgb(209, 72, 54); } a .gs_fsvg { border-bottom: 1px solid transparent; } a:hover .gs_fsvg, a:focus .gs_fsvg { border-bottom-color: inherit; } .gs_fsml { font-size: 13px; } .gs_fscp { font-variant: small-caps; } .gsh_clim { display: table-row; } .gsh_clil, .gsh_clic { display: table-cell; padding-bottom: 8px; } .gsh_clil { padding-right: 8px; } .gsh_lla { list-style-type: lower-alpha; } .gsh_lua { list-style-type: upper-alpha; } .gsh_llr { list-style-type: lower-roman; } .gsh_lur { list-style-type: upper-roman; } .gsh_l > li { margin-left: 32px; } .gs_fma .gsh_l > li { margin-left: 16px; } .gsh_h3 { font-size: inherit; font-weight: normal; } .gsh_h3, .gsh_csp { margin: 16px 0px; } .gsh_h3 + .gsh_csp { margin-top: -8px; } .gs_fma .gsh_h3 { margin: 12px 0px 0px; font-size: 17px; } .gs_fma_abs .gsh_csp { margin: 0px 0px 12px; } .gs_fma b, .gs_fma .gsh_h3 { color: rgb(68, 68, 68); } .gs_fma .gsh_h3 + .gsh_csp { margin: 4px 0px 0px; } .gs_fma .gsh_csp:last-child, .gs_fma .gsh_h3.gsh_flh { margin: 0px; } .gs_fma ul .gsh_clim:last-child .gsh_clic { padding-bottom: 0px; } .gs_fma ul, .gs_fma ol { margin: 12px 0px; } .gsh_ovln { text-decoration: overline; } .gsh_small .gsh_l .gsh_csp { margin: 8px 0px; } .gsh_small .gsh_csp:first-child, .gsh_small .gsh_h3.gsh_flh { margin-top: 0= px; } .gsh_small .gsh_csp:last-child { margin-bottom: 0px; } .gsh_dspfr { text-align: center; } .gsh_dspfr svg { margin: 8px 0px; } .gs_pp_tn, .gs_el_ta .gs_pp_mo_tn, .gs_el_ph .gs_pp_mo_tn { width: 32px; he= ight: 32px; } .gs_pp_sm, .gs_el_ta .gs_pp_mo_sm, .gs_el_ph .gs_pp_mo_sm { width: 56px; he= ight: 56px; } .gs_pp_nm, .gs_el_ta .gs_pp_mo_nm, .gs_el_ph .gs_pp_mo_nm { width: 128px; h= eight: 128px; } .gs_ai_pho { float: left; } .gs_ai_t { margin-left: 72px; } .gs_ai_pho_pst + .gs_ai_t { margin-left: 48px; } .gs_ai_t.gs_ai_pss { margin-left: 64px; } .gs_ai_pho_pst + .gs_ai_t.gs_ai_pss { margin-left: 40px; } .gs_ai_name { font-size: 17px; font-weight: normal; margin-bottom: 4px; } .gs_ai_name a { padding: 6px 0px 4px; } .gs_ai_name.gs_ai_name_nlsb { font-size: 15px; } .gs_ai_name.gs_ai_name_nlsb a:link, .gs_ai_name.gs_ai_name_nlsb a:visited {= padding: 7px 0px 5px; color: rgb(34, 34, 34); } .gs_ai_name.gs_ai_name_nlsb a:active { color: rgb(209, 72, 54); } .gs_ai_on { margin-bottom: 4px; } .gs_ai_on_lbl { color: rgb(119, 119, 119); } .gs_ai_int { margin-top: 5px; } .gs_ai_eml:empty, .gs_ai_int:empty, .gs_ai_cby:empty { display: none; } .gs_ai_one_int { vertical-align: top; font-size: 13px; margin-right: 8px; w= hite-space: nowrap; display: inline-block; max-width: 200px; text-overflow:= ellipsis; overflow: hidden; } .gs_el_tc a.gs_ai_one_int { padding: 8px 0px 5px; } .gs_el_ph .gs_ai_eml, .gs_el_ph .gs_ai_cby { margin-top: 8px; } .gs_ai_ilnl .gs_ai_int, .gs_ai_ilnl .gs_ai_cby { margin-top: 8px; color: rg= b(102, 102, 102); } .gs_ai.gs_ai_chpr { position: relative; } .gs_ai_chpr .gs_ai_t { margin-right: 276px; } .gs_el_sm .gs_ai_chpr .gs_ai_t { margin-right: 156px; } .gs_el_ph .gs_ai_chpr .gs_ai_t { margin-right: 0px; } .gs_ai_chpr .gs_ai_cby { position: absolute; top: 4px; right: 0px; text-ali= gn: right; } .gs_el_sm .gs_ai_chpr .gs_ai_cby, .gs_el_ta .gs_ai_chpr .gs_ai_cby { width:= 132px; overflow-wrap: break-word; } .gs_el_ph .gs_ai_chpr .gs_ai_cby { text-align: left; position: static; widt= h: auto; } #gsc_bdy { position: relative; max-width: 1200px; margin: 0px auto; } .gs_el_ph #gsc_bdy, .gs_el_ta #gsc_bdy { display: flex; flex-flow: column; = } .gsc_lcl { position: relative; margin: 0px 350px 0px 0px; order: 3; } .gs_el_sm .gsc_lcl { margin-right: 334px; } .gs_el_ta .gsc_lcl, .gs_el_ph .gsc_lcl { margin: 0px; } #gsc_prf_t_wrp { position: relative; order: 2; overflow: hidden; } .gs_el_tc #gsc_prf_t_wrp::after { display: block; content: ""; position: ab= solute; z-index: 100; top: 0px; right: 0px; width: 12px; height: 100%; back= ground-image: linear-gradient(to right, rgba(247, 247, 247, 0), rgb(247, 24= 7, 247) 80%); } #gsc_prf_t { width: 100%; background-color: rgb(245, 245, 245); display: no= ne; white-space: nowrap; overflow-x: auto; padding: 0px 4px; } .gs_el_ta #gsc_prf_t, .gs_el_ph #gsc_prf_t { display: block; } #gsc_prf_t::after { content: "=C2=A0"; padding: 0px 4px; } .gsc_prf_tab, .gsc_prf_tab:link { font-size: 15px; padding: 13px 12px; disp= lay: inline-block; color: rgb(102, 102, 102); cursor: pointer; } .gsc_prf_tab:hover { color: rgb(0, 0, 0); text-decoration: none; } .gsc_prf_tab:active { color: rgb(77, 144, 254); } .gsc_prf_tab[aria-selected=3D"true"] { border-bottom: 2px solid rgb(77, 144= , 254); color: rgb(4, 97, 249); cursor: default; } .gs_el_ta #gsc_art, .gs_el_ph #gsc_art, .gs_el_ta #gsc_rsb_cit, .gs_el_ph #= gsc_rsb_cit, .gs_el_ta #gsc_rsb_mnd, .gs_el_ph #gsc_rsb_mnd, .gs_el_ta #gsc= _rsb_awd, .gs_el_ph #gsc_rsb_awd, .gs_el_ta #gsc_rsb_co, .gs_el_ph #gsc_rsb= _co { display: none; } #gsc_bdy[data-tab=3D"gsc_prf_t-art"] #gsc_art, #gsc_bdy[data-tab=3D"gsc_prf= _t-cit"] #gsc_rsb_cit, #gsc_bdy[data-tab=3D"gsc_prf_t-mnd"] #gsc_rsb_mnd, #= gsc_bdy[data-tab=3D"gsc_prf_t-awd"] #gsc_rsb_awd, #gsc_bdy[data-tab=3D"gsc_= prf_t-ath"] #gsc_rsb_co { display: block; } .gsc_rsb { float: right; width: 317px; order: 4; border-left: 1px solid rgb= (238, 238, 238); margin-top: 32px; } .gs_el_sm .gsc_rsb { margin-top: 16px; } .gs_el_ph .gsc_rsb, .gs_el_ta .gsc_rsb { float: none; width: auto; border: = none; margin: 0px; } .gsc_rsb_s { margin: 0px 0px 48px 16px; position: relative; } .gs_el_sm .gsc_rsb_s { margin: 0px 0px 32px 16px; } .gs_el_ph .gsc_rsb_s, .gs_el_ta .gsc_rsb_s { margin: 0px; } .gsc_rsb_s:last-child { margin-bottom: 0px; } .gsc_rsb_header { padding: 8px 8px 12px; border-bottom: 1px solid rgb(229, = 229, 229); font-weight: normal; font-size: 15px; } .gs_el_sm .gsc_rsb_header { padding: 4px 8px 9px; } .gs_el_ph .gsc_rsb_header, .gs_el_ta .gsc_rsb_header { display: none; } .gsc_rsb_action { position: absolute; top: -3px; right: -2px; } .gs_el_sm .gsc_rsb_action { top: -8px; } .gsc_rsb_tap { display: block; position: absolute; right: 2px; top: 12px; o= pacity: 0.5; z-index: 1; } .gs_el_ta .gsc_rsb_tap, .gs_el_ph .gsc_rsb_tap { top: 24px; right: 10px; } .gsc_rsb_hm { border-bottom: 1px solid rgb(229, 229, 229); padding: 3px 6px= ; } #gsc_rsb_gpl { display: block; margin-top: 3px; padding: 6px 16px; line-hei= ght: 15px; color: rgb(4, 97, 249); border: 1px solid rgb(77, 144, 254); bor= der-radius: 2px; text-align: center; text-transform: uppercase; } .gs_el_sm #gsc_rsb_gpl { margin-top: 0px; } .gs_el_ta #gsc_rsb_gpl, .gs_el_ph #gsc_rsb_gpl { display: none; } #gsc_rsb_st { width: 100%; } .gsc_rsb_std { text-align: right; padding-right: 8px; } .gs_el_ta .gsc_rsb_std, .gs_el_ph .gsc_rsb_std { padding-right: 16px; } .gsc_rsb_sc1 { text-align: left; padding: 2px 8px; } .gs_el_sm .gsc_rsb_sc1 { padding: 0px 8px; } .gs_el_ta .gsc_rsb_sc1, .gs_el_ph .gsc_rsb_sc1 { padding: 4px 16px; } .gsc_rsb_sth { font-weight: normal; padding: 8px 8px 8px 0px; border-bottom= : 1px solid rgb(229, 229, 229); text-align: right; } .gs_el_sm .gsc_rsb_sth { padding: 4px 8px 4px 0px; } .gs_el_ta .gsc_rsb_sth, .gs_el_ph .gsc_rsb_sth { padding: 16px 16px 16px 0p= x; } #gsc_rsb_st tbody::before, #gsc_rsb_st tbody::after { content: ""; display:= block; height: 8px; } .gs_el_sm #gsc_rsb_st tbody::before, .gs_el_sm #gsc_rsb_st tbody::after { h= eight: 4px; } .gs_el_ph #gsc_hist_opn, .gs_el_ta #gsc_hist_opn { display: none; } .gsc_rsb_f { max-width: 118px; overflow-wrap: break-word; white-space: norm= al; } .gs_el_ta .gsc_rsb_f { max-width: none; } .gsc_rsb_f:link, .gsc_rsb_f:visited { color: rgb(34, 34, 34); } .gsc_rsb_m_na { color: rgb(221, 75, 57); } .gsc_rsb_m_a { color: rgb(0, 102, 33); float: right; position: relative; } .gsc_rsb_m_bar { width: 100%; height: 4px; margin: 8px 0px; background: rgb= (0, 102, 33); } .gsc_rsb_m_bar_na { background: rgb(221, 75, 57); width: 100%; height: 100%= ; z-index: 1; } .gsc_rsb_m { padding: 8px; } .gs_el_ta .gsc_rsb_m, .gs_el_ph .gsc_rsb_m { padding: 8px 16px; } .gsc_rsb_m_desc { padding-top: 16px; color: rgb(119, 119, 119); } .gsc_rsb_m_s { font-size: 24px; position: absolute; line-height: 0.3; } #gsc_lwp_mndt_lnk { text-transform: uppercase; margin-left: 16px; margin-ri= ght: -9px; text-align: right; font-size: 13px; padding: 12px 9px; border-ra= dius: 3px; } #gsc_lwp_mndt_lnk:hover, #gsc_lwp_mndt_lnk:active, #gsc_lwp_mndt_lnk:visite= d { text-decoration: none; color: rgb(26, 13, 171); } #gsc_lwp_mndt_lnk:hover { background-color: rgba(0, 0, 0, 0.05); } #gsc_lwp_mndt_lnk:active { background-color: rgba(0, 0, 0, 0.1); } .gsc_rsb_m_title { padding-bottom: 12px; } .gsc_rsb_m_header { display: flex; align-items: flex-end; justify-content: = space-between; padding: 0px 8px; } .gs_el_sm .gsc_rsb_m_header { padding: 0px 8px; } .gsc_rsb_hmv { text-align: center; padding-top: 16px; } .gsc_rsb_a { list-style: none; } .gsc_rsb_a > li { position: relative; } .gs_el_ta .gsc_rsb_a > li, .gs_el_ph .gsc_rsb_a > li { border-bottom: 1px s= olid rgb(229, 229, 229); } .gsc_rsb_a > li:first-child { margin-top: 8px; } .gsc_rsb_a_pht { float: left; width: 32px; height: 32px; } .gsc_rsb_a_desc { margin: 0px 33px 0px 48px; min-height: 32px; display: blo= ck; } .gs_el_ph .gsc_rsb_a_desc, .gs_el_ta .gsc_rsb_a_desc { margin: 0px 33px 0px= 64px; min-height: 56px; } .gsc_rsb_a_desc a { color: rgb(34, 34, 34); } .gsc_rsb_a_ext { display: block; color: rgb(119, 119, 119); font-size: 13px= ; white-space: nowrap; overflow: hidden; text-overflow: ellipsis; } .gs_el_ph .gsc_rsb_a_ext, .gs_el_ta .gsc_rsb_a_ext { white-space: normal; } .gsc_rsb_a_ext2 { display: none; } .gs_el_ph .gsc_rsb_a_ext2, .gs_el_ta .gsc_rsb_a_ext2 { display: block; } .gsc_rsb_aa { display: block; padding: 8px; line-height: normal; cursor: po= inter; } .gs_el_ph .gsc_rsb_aa, .gs_el_ta .gsc_rsb_aa { font-size: 17px; padding: 12= px 16px; } .gsc_rsb_aa:hover, .gsc_rsb_aa:active { text-decoration: none; background: = rgb(241, 241, 241); } .gsc_rsb_aa a:hover { text-decoration: none; } #gsc_prf_w { order: 1; padding: 32px 0px; overflow: hidden; } .gs_el_sm #gsc_prf_w { padding: 16px 0px; } #gsc_prf_pu { float: left; width: 128px; height: 128px; text-align: center;= } .gs_el_ph #gsc_prf_pu { float: none; width: 100%; margin: 0px 0px 12px; } #gsc_prf_pua { line-height: 0; width: 128px; height: 128px; } .gs_el_ph #gsc_prf_pua { margin: 0px 16px; } #gsc_prf_pufi { width: 0px; height: 0px; overflow: hidden; } .gsc_prf_pufo #gsc_prf_pufi { width: auto; height: auto; overflow: visible;= position: relative; z-index: 10; } .gsc_prf_pufo #gsc_prf_pufi2 { display: inline-block; background: rgb(252, = 252, 252); padding: 8px 8px 8px 0px; } .gsc_prf_puic { position: absolute; bottom: 0px; width: 100%; padding: 8px = 0px; background-color: rgb(0, 0, 0); opacity: 0.6; } .gsc_prf_pel { cursor: pointer; } #gsc_prf_i { margin: 0px 16px 0px 160px; } .gs_el_sm #gsc_prf_i { margin: 0px 16px 0px 144px; } .gs_el_ph #gsc_prf_i { margin: 0px 16px; } a.gsc_prf_btn { position: relative; display: inline-flex; align-items: cent= er; height: 29px; line-height: normal; text-align: center; padding: 0px 8px= ; color: rgb(68, 68, 68); font-weight: normal; font-size: 13px; } a.gsc_prf_btn::before { position: absolute; inset: 0px; z-index: -1; conten= t: ""; border-radius: 3px; } .gsc_prf_btn .gs_ico { opacity: 0.55; } .gsc_prf_btn:hover .gs_ico { opacity: 0.72; } .gsc_prf_btn_act .gs_ico, .gsc_prf_btn_act:hover .gs_ico, .gsc_prf_btn:acti= ve .gs_ico { opacity: 1; } .gsc_prf_btn_lbl { padding: 0px 4px; } a.gsc_prf_btn:hover { text-decoration: none; cursor: pointer; color: rgb(34= , 34, 34); } a.gsc_prf_btn:hover::before, .gs_el_ph #gsc_prf_btne:hover::before { border= -color: rgba(0, 0, 0, 0.2); background-color: rgb(248, 248, 248); } a.gsc_prf_btn:active::before, .gs_el_ph #gsc_prf_btne:active::before { back= ground-color: rgb(241, 241, 241); color: rgb(34, 34, 34); } .gs_el_ph .gsc_prf_btn_lbl { display: none; } .gs_el_ph .gsc_prf_btn, #gsc_prf_btne { justify-content: center; height: 41= px; width: 41px; padding: 0px; } .gs_el_ph .gsc_prf_btn, .gs_el_ph #gsc_prf_btne { position: absolute; right= : 8px; margin: 0px; } .gs_el_ph .gsc_prf_btn::before { border-radius: 50%; inset: 2px; } #gsc_prf_btne { vertical-align: top; margin: -9px 4px; } .gs_el_ph #gsc_prf_btne { top: 14px; } #gsc_prf_btne.gsc_prf_btn::before { background: transparent; border: transp= arent; } #gsc_prf_btne.gsc_prf_btn:hover::before { background-color: rgba(0, 0, 0, 0= .05); } #gsc_prf_btne.gsc_prf_btn:active::before { background-color: rgba(0, 0, 0, = 0.1); } #gsc_prf_btnf { float: right; margin: 3px 0px 16px 16px; } .gs_el_sm #gsc_prf_btnf { margin-top: 0px; } .gs_el_ph #gsc_prf_btnf { float: none; top: 62px; } .gs_el_ph #gsc_prf_btnf.gsc_prf_btn_top { top: 14px; } .gsc_prf_btn::before, .gs_el_ph #gsc_prf_btne::before { background-color: r= gb(245, 245, 245); border: 1px solid rgba(0, 0, 0, 0.1); } .gsc_prf_btn.gsc_prf_btn_act::before { background-color: rgb(77, 144, 254);= } .gsc_prf_btn_act .gsc_prf_btn_lbl { color: rgb(255, 255, 255); -webkit-font= -smoothing: antialiased; } .gsc_prf_btn.gsc_prf_btn_act:hover::before { background-color: rgb(57, 131,= 254); } .gsc_prf_btn.gsc_prf_btn_act:active::before { background-color: rgb(53, 122= , 232); } #gsc_prf_inw { margin-bottom: 8px; } .gs_el_sm #gsc_prf_inw { margin-bottom: 4px; } .gs_el_ph #gsc_prf_inw { margin-bottom: 2px; } #gsc_prf_in { font-size: 24px; line-height: 24px; padding: 3px 0px 4px; ove= rflow-wrap: break-word; } .gs_el_sm #gsc_prf_in { font-size: 22px; } .gs_el_ph #gsc_prf_in { font-size: 20px; padding: 0px; } #gsc_prf_ion { line-height: 19px; padding: 1px 0px; } #gsc_prf_ion { padding-bottom: 4px; } .gs_el_sm #gsc_prf_ion { padding-bottom: 8px; } .gs_el_ph #gsc_prf_ion { padding: 0px 0px 10px; } #gsc_prf_ion_btn, #gs_prf_ion_txt { vertical-align: top; } #gsc_prf_ion_btn { padding: 12px 0px; } #gsc_prf_ion_lbl { color: rgb(119, 119, 119); } .gs_prf_ion_svg { height: 8px; width: 4px; padding: 0px 4px; fill: rgb(119,= 119, 119); } #gsc_prf_ion_btn:hover #gsc_prf_ion_lbl { cursor: pointer; text-decoration:= underline; } #gsc_prf_ion_btn:active #gsc_prf_ion_lbl { color: rgb(34, 34, 34); } #gsc_prf_ion_btn:active .gs_prf_ion_svg { fill: rgb(34, 34, 34); } #gs_prf_ion_txt { padding-left: 4px; visibility: hidden; font-size: 0px; op= acity: 0; transition: visibility 0.13s, font-size 0.13s, opacity 0.13s; } .gs_el_tc #gs_prf_ion_txt { transition: visibility 0.218s, font-size 0.218s= , opacity 0.218s; } .gs_prf_ion_show #gs_prf_ion_txt { visibility: visible; font-size: 15px; op= acity: 1; transition: visibility, font-size, opacity 0.13s; } .gs_el_tc .gs_prf_ion_show #gs_prf_ion_txt { transition: visibility, font-s= ize, opacity 0.218s; } .gs_prf_ion_svg { transition: transform 0.13s; transform: rotate(0deg); } .gs_el_tc .gs_prf_ion_svg { transition: transform 0.218s; } .gs_prf_ion_show .gs_prf_ion_svg { transform: rotate(180deg); } .gsc_prf_il { font-size: 15px; line-height: 18px; padding: 1px 0px; } .gs_el_ph .gsc_prf_il { font-size: 13px; line-height: 16px; } .gsc_prf_ila:link, .gsc_prf_ila:visited { text-decoration: underline; color= : rgb(34, 34, 34); } #gsc_prf_int { margin-top: 5px; } #gsc_prf_int:empty { display: none; } .gsc_prf_inta { margin-right: 16px; white-space: nowrap; max-width: 200px; = text-overflow: ellipsis; overflow: hidden; vertical-align: top; } .gsc_prf_inta:last-child { margin: 0px; } .gs_el_tc .gsc_prf_ila, .gs_el_tc .gsc_prf_inta { padding: 8px 0px 5px; } .gsc_md_pro_tt, #gsc_md_pro_lgtm, #gsc_md_pro_rev_n, #gsc_md_pro_save, #gsc= _md_pro_ep, .gsc_md_pro_el, .gsc_md_pro_ev { display: none; } #gsc_md_pro_ep { margin-top: 4px; padding-top: 16px; border-top: 1px solid = rgb(229, 229, 229); } #gsc_md_pro_ep .gsc_md_pro_el { margin-bottom: 8px; } .gs_el_tc #gsc_md_pro_ep .gsc_md_pro_el { margin-bottom: 2px; } .gsc_md_pro_ra .gs_md_btns { margin-top: 8px; } .gsc_md_pro_rn #gs_aani_wrap { padding: 0px; border: none; } .gsc_md_pro_rn .gs_md_btns { margin: 0px; } .gsc_md_pro_ch .gsc_md_pro_el, .gsc_md_pro_ch .gsc_md_pro_ev, .gsc_md_pro_c= h #gsc_md_pro_ep, .gsc_md_pro_ra .gsc_md_pro_ra_vis, .gsc_md_pro_rn .gsc_md= _pro_rn_vis { display: block; } .gsc_md_pro_ed #gsc_md_pro_ted, .gsc_md_pro_aa #gsc_md_pro_taa, .gsc_md_pro= _ra #gsc_md_pro_tra, .gsc_md_pro_an #gsc_md_pro_tan, .gsc_md_pro_ai #gsc_md= _pro_tai, .gsc_md_pro_rn #gsc_md_pro_trn, .gsc_md_pro_ch #gsc_md_pro_save, = .gsc_md_pro_ra #gsc_md_pro_lgtm, .gsc_md_pro_rn #gsc_md_pro_rev_n { display= : inline-block; } .gsc_md_pro_el { font-size: 15px; } .gsc_md_pro_ev { padding: 4px 0px 16px; } #gsc_dd_add-d, #gsc_dd_exp-d, #gsc_dd_sort-d, #gsc_dd_mor-d { top: 42px; } #gsc_dd_add-d, #gsc_dd_mor-d, #gsc_dd_sort-d { white-space: normal; overflo= w-wrap: break-word; width: max-content; min-width: 100px; max-width: 208px;= } .gs_el_ph #gsc_dd_add-d { left: -9px; } .gs_el_ph #gsc_dd_exp-d { left: auto; right: 12px; } .gs_el_ph #gsc_dd_mor-d { left: -58px; } .gs_el_ph #gsc_dd_sort-d { left: 10px; } .gs_el_ph #gsc_dd_sort-r { margin-left: -10px; } .gsc_dd_sec, #gsc_dd_exp-d { padding: 8px 0px; } .gs_el_tc .gsc_dd_sec, .gs_el_tc #gsc_dd_exp-d { padding: 4px 0px 8px; } .gsc_dd_sep { border-top: 1px solid rgb(235, 235, 235); } #gsc_dd_mor-s .gsc_dd_mor-sel, #gsc_dd_sort-s .gsc_dd_sort-sel { color: rgb= (221, 75, 57); } #gsc_dd_mor-p { padding: 14px 44px 14px 16px; color: rgb(119, 119, 119); } .gs_el_tc #gsc_dd_mor-p { padding: 18px 44px 18px 16px; } .gsc_art_sel #gsc_dd_add-r, .gsc_art_sel #gsc_dd_mor-r, #gsc_btn_mer, #gsc_= btn_del, #gsc_dd_exp-r { display: none; } #gsc_dd_mor-r, .gsc_art_sel #gsc_btn_mer, .gsc_art_sel #gsc_btn_del, .gsc_a= rt_sel #gsc_dd_exp-r { display: inline-block; } html:not(.gs_el_ph) #gsc_dd_sort-r { display: none; } #gsc_lwp { margin: 24px 0px; text-align: center; } .gs_el_sm #gsc_lwp { margin: 16px 0px; } #gsc_bpf { display: inline-block; } #gsc_a_nn { display: inline-block; vertical-align: middle; padding-right: 1= 6px; font-size: 13px; } .gs_el_ph #gsc_a_nn { display: none; } @media print { #gs_top #gs_md_s, #gs_top #gs_md_w, #gs_top #gs_hdr, #gs_top #gs_hdr_drs,= #gs_top #gs_hdr_drw, #gs_top #gs_ftr, #gs_top #gsc_nag, #gs_top #gsc_prf_n= bar_btns, #gs_top #gsc_prf_btne, #gs_top #gsc_prf_btnf, #gs_top #gsc_prf_iv= h, #gs_top #gsc_prf_puf, #gs_top #gsc_rsb_co, #gs_top #gsc_bdy #gsc_rsb_co,= #gs_top .gsc_g_hist_wrp, #gs_top #gsc_prf_t_wrp, #gs_top .gsc_rsb_header, = #gs_top .gsc_a_tb, #gs_top .gsc_a_x, #gs_top #gsc_lwp, #gs_top .gsc_prf_pui= c, #gs_top #gsc_dd_add-r, #gs_top #gsc_dd_mor-r, #gs_top #gsc_dd_sort-r { d= isplay: none; } #gs_top, #gs_top #gsc_bdy, #gs_top #gsc_prf_w, #gs_top #gsc_prf, #gs_top = #gsc_prf_pu, #gs_top #gsc_prf_pua, #gs_top #gsc_prf_i, #gs_top .gsc_rsb_s, = #gs_top .gsc_lcl, #gs_top .gsc_rsb, #gs_top #gsc_a_tw, #gs_top #gsc_a_t, #g= s_top .gsc_prf_il, #gs_top .gsc_prf_ila, #gs_top .gsc_prf_inta, #gs_top #gs= c_rsb_st { background: none; border: none; padding: 0px; margin: 0px; heigh= t: auto; width: auto; min-width: 0px; max-width: none; float: none; display= : block; position: static; color: black; font-weight: normal; font-size: 12= pt; text-decoration: none; } #gs_top .gsc_a_ac, #gs_top .gsc_a_a, #gs_top #gsc_a_ca, #gs_top .gsc_a_at= , #gs_top .gsc_rsb_sc1, #gs_top .gsc_rsb_sth, #gs_top .gsc_rsb_std, #gs_top= #gsc_bdy .gsc_a_x, #gs_top #gsc_bdy .gsc_a_t, #gs_top #gsc_bdy .gsc_a_c, #= gs_top #gsc_bdy .gsc_a_y, #gs_top #gsc_a_trh, #gs_top .gsc_a_m, #gs_top .gs= c_a_am { color: black; font-weight: normal; font-size: 12pt; padding: 0px; = margin: 0px; background: none; border: none; } #gs_top #gsc_a_trh, #gs_top #gsc_a_trh th { height: 0px; } #gs_top #gsc_a_ta, #gs_top #gsc_a_ca, #gs_top #gsc_a_ha, #gs_top .gsc_a_a= { font-size: 11pt; } #gs_top .gsc_a_ac { font-size: 10pt; } #gs_top #gsc_prf_pu { width: 80pt; height: auto; float: left; margin: 0px= 7pt 7pt 0px; } #gs_top #gsc_prf_pua { left: auto; transform: none; border-radius: 0px; } #gs_top #gsc_prf_pua > img { position: static; } #gs_top #gsc_prf_i { margin: 0px 7pt 7pt 87pt; text-align: left; } #gs_top #gsc_prf_in { font-size: 18pt; line-height: 18pt; padding: 0px 0p= x 4pt; } #gs_top .gsc_prf_il { padding: 2pt 0px; } #gs_top #gsc_prf_w { float: left; width: 64%; } #gs_top .gsc_rsb { float: right; width: 35%; } #gs_top #gsc_art { clear: both; } #gs_top #gsc_rsb_st { display: table; width: 100%; max-width: none; margi= n-top: 3pt; } #gs_top .gsc_rsb_sc1, #gs_top .gsc_rsb_sth, #gs_top .gsc_rsb_std { font-s= ize: 10pt; } #gs_top th.gsc_rsb_sc1, #gs_top .gsc_rsb_sth { border-bottom: 1pt solid r= gb(204, 204, 204); } #gs_top .gsc_rsb_f { max-width: 60pt; } #gs_top .gsc_rsb_sth { padding-left: 14pt; } #gs_top #gsc_bdy .gsc_a_x, #gs_top #gsc_bdy .gsc_a_t, #gs_top #gsc_bdy .g= sc_a_c, #gs_top #gsc_bdy .gsc_a_y, #gs_top #gsc_a_trh { padding: 6pt 0px; } #gs_top #gsc_a_trh { border-bottom: 1pt solid rgb(204, 204, 204); } #gs_top #gsc_a_ca { display: block; width: auto; } #gs_top #gsc_a_ta { display: inline-block; vertical-align: middle; margin= -right: 12pt; } #gs_top .gsc_a_h { display: inline; font-size: 10pt; } #gs_top .gsc_a_at { color: rgb(0, 0, 136); } #gs_top .gsc_a_m, #gs_top .gsc_a_am { display: inline; position: absolute= ; } #gs_top .gsc_a_am { padding: 11pt 0px 0px 8pt; } #gs_top .gsc_a_t .gs_gray { color: black; font-size: 10pt; } } .gsc_lwpds_frm { position: relative; height: 29px; } .gsc_lwpds_tsiw { position: absolute; top: 0px; left: 0px; right: 38px; } .gsc_lwpds_tsiw input { border-radius: 3px 0px 0px 3px; } .gsc_lwpds_tsbw { position: absolute; top: 0px; right: 0px; } .gsc_lwpds_tsbw button { border-radius: 0px 3px 3px 0px; } .gsc_pgn { text-align: right; font-weight: bold; line-height: 29px; } .gsc_pgn_ppn { margin: 0px 8px; } .gsc_ccb_ck { padding: 11px 10px 9px; } .gsc_ccb_svg { stroke: rgb(102, 102, 102); stroke-width: 2px; fill: rgb(255= , 255, 255); width: 21px; height: 21px; vertical-align: top; } .gsc_ccb_lim, .gsc_ccb_dis { cursor: default; pointer-events: none; } .gsc_ccb_lim .gsc_ccb_svg, .gsc_ccb_dis .gsc_ccb_svg { fill: rgb(226, 226, = 226); stroke: rgb(255, 255, 255); } .gsc_ccb_lim .gsc_ccb_svg > circle, .gsc_ccb_dis .gsc_ccb_svg > circle { st= roke: rgb(226, 226, 226); } .gsc_ccb_on .gsc_ccb_svg { fill: rgb(77, 144, 254); stroke: rgb(255, 255, 2= 55); } .gsc_ccb_on .gsc_ccb_svg > circle { stroke: rgb(77, 144, 254); } .gsc_ccb_del:active .gsc_ccb_svg > circle, 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