Jinyuan Chang (常晋源)

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Qiwei YaoLondon School of EconomicsVerificeret mail på lse.ac.uk
Wen ZhouNew York UniversityVerificeret mail på nyu.edu
Eric KolaczykProfessor, Department of Mathematics & Statistics, McGill UniversityVerificeret mail på mcgill.ca
Tong Tong WuUniversity of RochesterVerificeret mail på urmc.rochester.edu
Tao ZouThe Australian National UniversityVerificeret mail på anu.edu.au
Yumou QiuIowa State UniversityVerificeret mail på iastate.edu
Chao ZhengUniversity of SouthamptonVerificeret mail på southampton.ac.uk
Wenxin ZhouDepartment of Information & Decision Sciences, University of Illinois ChicagoVerificeret mail på uic.edu
Qi-Man SHAOThe Chinese University of Hong KongVerificeret mail på cuhk.edu.hk
Lan WangCentennial Endowed Professor, University of MiamiVerificeret mail på mbs.miami.edu
Xinghao QiaoThe University of Hong KongVerificeret mail på hku.hk
Xiaohong ChenProfessor of Economics, Yale UniversityVerificeret mail på yale.edu
Xiaofeng ShaoDepartment of Statistics and Data Science, and Dept of Economics, Washington University in St LouisVerificeret mail på wustl.edu
Xiaohui ChenUniversity of Southern CaliforniaVerificeret mail på usc.edu
Cheng ChenSouthwestern University of Finance and EconomicsVerificeret mail på swufe.edu.cn
Aurore DelaigleUniversity of MelbourneVerificeret mail på unimelb.edu.au
Zhentao ShiChinese University of Hong KongVerificeret mail på cuhk.edu.hk
Tucker McElroyU.S. Census BureauVerificeret mail på census.gov
Jian KangUniversity of MichiganVerificeret mail på umich.edu
Cheng Yong Tang (汤琤咏)Professor of Statistics, Temple UniversityVerificeret mail på temple.edu

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Jinyuan Chang (常晋源)

Southwestern University of Finance and Economics, Chinese Academy of Sciences

Verificeret mail på swufe.edu.cn - Startside

Empirical Likelihood Financial Econometrics High Dimensional Data Analysis Network Data Analysis


Titel Sortér efter henvisninger Sortér efter årstal Sortér efter titel	Citeret af Citeret af	År
Marginal empirical likelihood and sure independence feature screening J Chang, CY Tang, Y Wu The Annals of Statistics 41 (4), 2123-2148, 2013	115	2013
Principal component analysis for second-order stationary vector time series J Chang, B Guo, Q Yao The Annals of Statistics 46 (5), 2094-2124, 2018	71*	2018
High dimensional stochastic regression with latent factors, endogeneity and nonlinearity J Chang, B Guo, Q Yao Journal of Econometrics 189 (2), 297-312, 2015	65	2015
Simulation-based hypothesis testing of high dimensional means under covariance heterogeneity J Chang, C Zheng, WX Zhou, W Zhou Biometrics 73 (4), 1300-1310, 2017	64	2017
Testing for high-dimensional white noise using maximum cross-correlations J Chang, Q Yao, W Zhou Biometrika 104 (1), 111-127, 2017	63	2017
Comparing large covariance matrices under weak conditions on the dependence structure and its application to gene clustering J Chang, W Zhou, WX Zhou, L Wang Biometrics 73 (1), 31-41, 2017	62	2017
A new scope of penalized empirical likelihood with high-dimensional estimating equations J Chang, CY Tang, TT Wu The Annals of Statistics 46 (6B), 3185-3216, 2018	58	2018
High dimensional generalized empirical likelihood for moment restrictions with dependent data J Chang, SX Chen, X Chen Journal of Econometrics 185 (1), 283-304, 2015	58	2015
On the approximate maximum likelihood estimation for diffusion processes J Chang, SX Chen The Annals of Statistics 39 (6), 2820-2851, 2011	57	2011
Double-bootstrap methods that use a single double-bootstrap simulation J Chang, P Hall Biometrika 102 (1), 203-214, 2015	49	2015
Local independence feature screening for nonparametric and semiparametric models by marginal empirical likelihood J Chang, CY Tang, Y Wu The Annals of Statistics 44 (2), 515-539, 2016	48	2016
Confidence regions for entries of a large precision matrix J Chang, Y Qiu, Q Yao, T Zou Journal of Econometrics 206 (1), 57-82, 2018	46	2018
Modelling matrix time series via a tensor CP-decomposition J Chang, J He, L Yang, Q Yao Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2023	43	2023
Central limit theorems for high dimensional dependent data J Chang, X Chen, M Wu Bernoulli 30 (1), 712-742, 2024	40	2024
Estimation of subgraph densities in noisy networks J Chang, ED Kolaczyk, Q Yao Journal of the American Statistical Association 117 (537), 361-374, 2022	37*	2022
High-dimensional empirical likelihood inference J Chang, SX Chen, CY Tang, TT Wu Biometrika 108 (1), 127-147, 2021	27	2021
Cram\'er-type moderate deviations for Studentized two-sample -statistics with applications J Chang, QM Shao, WX Zhou The Annals of Statistics 44 (5), 1931-1956, 2016	27	2016
An autocovariance-based learning framework for high-dimensional functional time series J Chang, C Chen, X Qiao, Q Yao Journal of Econometrics 239 (2), 105385, 2024	22	2024
Testing the martingale difference hypothesis in high dimension J Chang, Q Jiang, X Shao Journal of Econometrics 235 (2), 972-1000, 2023	15	2023
Optimal covariance matrix estimation for high-dimensional noise in high-frequency data J Chang, Q Hu, C Liu, CY Tang Journal of Econometrics 239 (2), 105329, 2024	13	2024

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