224 citations · 344 across the 14 of their papers we have counts for
15 papers
Simultaneous Inference in Multiple Matrix-Variate Graphs for High-Dimensional Neural Recordings
Zongge Liu, Heejong Bong, Zhao Ren +2
We study simultaneous inference for multiple matrix-variate Gaussian graphical models in high-dimensional settings. Such models arise when spatiotemporal data are collected across…
Mini-batch Estimation for Deep Cox Models: Statistical Foundations and Practical Guidance
Lang Zeng, Weijing Tang, Zhao Ren +1
The stochastic gradient descent (SGD) algorithm has been widely used to optimize deep Cox neural network (Cox-NN) by updating model parameters using mini-batches of data. We show t…
Outcome-Guided Disease Subtyping for High-Dimensional Omics Data
Peng Liu, Yusi Fang, Zhao Ren +2
High-throughput microarray and sequencing technology have been used to identify disease subtypes that could not be observed otherwise by using clinical variables alone. The classic…
User-Friendly Covariance Estimation for Heavy-Tailed Distributions
Yuan Ke, Stanislav Minsker, Zhao Ren +2
We offer a survey of recent results on covariance estimation for heavy-tailed distributions. By unifying ideas scattered in the literature, we propose user-friendly methods that fa…
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…
Variable screening with multiple studies
Tianzhou Ma, Zhao Ren, George C. Tseng
Advancement in technology has generated abundant high-dimensional data that allows integration of multiple relevant studies. Due to their huge computational advantage, variable scr…
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…
Tuning-Free Heterogeneity Pursuit in Massive Networks
Zhao Ren, Yongjian Kang, Yingying Fan +1
Heterogeneity is often natural in many contemporary applications involving massive data. While posing new challenges to effective learning, it can play a crucial role in powering m…
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…
Asymptotically Normal and Efficient Estimation of Covariate-Adjusted Gaussian Graphical Model
Mengjie Chen, Zhao Ren, Hongyu Zhao +1
A tuning-free procedure is proposed to estimate the covariate-adjusted Gaussian graphical model. For each finite subgraph, this estimator is asymptotically normal and efficient. As…
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].