224 citations · 344 across the 14 of their papers we have counts for
8 papers · 1 filter
Minimax Estimation of Large Precision Matrices with Bandable Cholesky Factor
Yu Liu, Zhao Ren
Last decade witnesses significant methodological and theoretical advances in estimating large precision matrices. In particular, there are scientific applications such as longitudi…
Pairwise Difference Estimation of High Dimensional Partially Linear Model
Fang Han, Zhao Ren, Yuxin Zhu
This paper proposes a regularized pairwise difference approach for estimating the linear component coefficient in a partially linear model, with consistency and exact rates of conv…
A General Decision Theory for Huber's -Contamination Model
Mengjie Chen, Chao Gao, Zhao Ren
Today's data pose unprecedented challenges to statisticians. It may be incomplete, corrupted or exposed to some unknown source of contamination. We need new methods and theories to…
Robust Covariance and Scatter Matrix Estimation under Huber's Contamination Model
Mengjie Chen, Chao Gao, Zhao Ren
Covariance matrix estimation is one of the most important problems in statistics. To accommodate the complexity of modern datasets, it is desired to have estimation procedures that…
Minimax estimation in sparse canonical correlation analysis
Chao Gao, Zongming Ma, Zhao Ren +1
Canonical correlation analysis is a widely used multivariate statistical technique for exploring the relation between two sets of variables. This paper considers the problem of est…
Sparse CCA via Precision Adjusted Iterative Thresholding
Mengjie Chen, Chao Gao, Zhao Ren +1
Sparse Canonical Correlation Analysis (CCA) has received considerable attention in high-dimensional data analysis to study the relationship between two sets of random variables. Ho…
Asymptotic normality and optimalities in estimation of large Gaussian graphical models
Zhao Ren, Tingni Sun, Cun-Hui Zhang +1
The Gaussian graphical model, a popular paradigm for studying relationship among variables in a wide range of applications, has attracted great attention in recent years. This pape…
Discussion: Latent variable graphical model selection via convex optimization
Zhao Ren, Harrison H. Zhou
Discussion of "Latent variable graphical model selection via convex optimization" by Venkat Chandrasekaran, Pablo A. Parrilo and Alan S. Willsky [arXiv:1008.1290].