Marginal empirical likelihood and sure independence feature screening J Chang, CY Tang, Y Wu The Annals of Statistics 41 (4), 2123-2148, 2013 | 108 | 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 | 62* | 2018 |
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 | 58 | 2017 |
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 |
Testing for high-dimensional white noise using maximum cross-correlations J Chang, Q Yao, W Zhou Biometrika 104 (1), 111-127, 2017 | 55 | 2017 |
High dimensional stochastic regression with latent factors, endogeneity and nonlinearity J Chang, B Guo, Q Yao Journal of Econometrics 189 (2), 297-312, 2015 | 55 | 2015 |
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 | 53 | 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 | 52 | 2018 |
On the approximate maximum likelihood estimation for diffusion processes J Chang, SX Chen The Annals of Statistics 39 (6), 2820-2851, 2011 | 50 | 2011 |
Double-bootstrap methods that use a single double-bootstrap simulation J Chang, P Hall Biometrika 102 (1), 203-214, 2015 | 46 | 2015 |
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 | 44 | 2018 |
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 | 43 | 2016 |
Estimation of subgraph densities in noisy networks J Chang, ED Kolaczyk, Q Yao Journal of the American Statistical Association 117 (537), 361-374, 2022 | 33* | 2022 |
Central limit theorems for high dimensional dependent data J Chang, X Chen, M Wu Bernoulli 30 (1), 712-742, 2024 | 30 | 2024 |
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 | 23 | 2023 |
High-dimensional empirical likelihood inference J Chang, SX Chen, CY Tang, TT Wu Biometrika 108 (1), 127-147, 2021 | 23 | 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 | 23 | 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 | 20 | 2024 |
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 | 11 | 2024 |
Testing the martingale difference hypothesis in high dimension J Chang, Q Jiang, X Shao Journal of Econometrics 235 (2), 972-1000, 2023 | 9 | 2023 |