受强制性开放获取政策约束的文章 - Ziwei Zhu了解详情
可在其他位置公开访问的文章：9 篇
Distributed testing and estimation under sparse high dimensional models
H Battey, J Fan, H Liu, J Lu, Z Zhu
Annals of statistics 46 (3), 1352, 2018
强制性开放获取政策： US National Science Foundation, US National Institutes of Health
Distributed estimation of principal eigenspaces
J Fan, D Wang, K Wang, Z Zhu
Annals of statistics 47 (6), 3009, 2019
强制性开放获取政策： US National Science Foundation, US National Institutes of Health
A shrinkage principle for heavy-tailed data: High-dimensional robust low-rank matrix recovery
J Fan, W Wang, Z Zhu
Annals of statistics 49 (3), 1239, 2021
强制性开放获取政策： US National Science Foundation, US National Institutes of Health
Generalized high-dimensional trace regression via nuclear norm regularization
J Fan, W Gong, Z Zhu
Journal of econometrics 212 (1), 177-202, 2019
强制性开放获取政策： US National Science Foundation
Robust high dimensional factor models with applications to statistical machine learning
J Fan, K Wang, Y Zhong, Z Zhu
Statistical science: a review journal of the Institute of Mathematical …, 2021
强制性开放获取政策： US National Science Foundation, US National Institutes of Health
High-dimensional principal component analysis with heterogeneous missingness
Z Zhu, T Wang, RJ Samworth
Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2022
强制性开放获取政策： US National Science Foundation, UK Engineering and Physical Sciences …
Learning Markov models via low-rank optimization
Z Zhu, X Li, M Wang, A Zhang
Operations Research 70 (4), 2384-2398, 2022
强制性开放获取政策： US National Science Foundation, US Department of Defense, 国家自然科学基金委员会
Taming heavy-tailed features by shrinkage
Z Zhu, W Zhou
International Conference on Artificial Intelligence and Statistics, 3268-3276, 2021
强制性开放获取政策： US National Science Foundation
Heterogeneity adjustment with applications to graphical model inference
J Fan, H Liu, W Wang, Z Zhu
Electronic journal of statistics 12 (2), 3908, 2018
强制性开放获取政策： US National Science Foundation, US National Institutes of Health
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