653 citations · 671 across the 6 of their papers we have counts for
7 papers
An Approximate Bayesian Approach to Covariate-dependent Graphical Modeling
Sutanoy Dasgupta, Peng Zhao, Jacob Helwig +3
Gaussian graphical models typically assume a homogeneous structure across all subjects, which is often restrictive in applications. In this article, we propose a weighted pseudo-li…
Matrix Quantile Factor Model
Xin-Bing Kong, Yong-Xin Liu, Long Yu +1
This paper introduces a matrix quantile factor model for matrix-valued data with low-rank structure. We estimate the row and column factor spaces via minimizing the empirical check…
Testing the number of common factors by bootstrapped sample covariance matrix in high-dimensional factor models
Long Yu, Peng Zhao, Wang Zhou
This paper studies the impact of bootstrap procedure on the eigenvalue distributions of the sample covariance matrix under a high-dimensional factor structure. We provide asymptoti…
Covariance Estimation and its Application in Large-Scale Online Controlled Experiments
Tao Xiong, Yihan Bao, Penglei Zhao +1
During the last few decades, online controlled experiments (also known as A/B tests) have been adopted as a golden standard for measuring business improvements in industry. In our…
High-Dimensional Linear Regression via Implicit Regularization
Peng Zhao, Yun Yang, Qiao-Chu He
Many statistical estimators for high-dimensional linear regression are M-estimators, formed through minimizing a data-dependent square loss function plus a regularizer. This work c…
The composite absolute penalties family for grouped and hierarchical variable selection
Peng Zhao, Guilherme Rocha, Bin Yu
Extracting useful information from high-dimensional data is an important focus of today's statistical research and practice. Penalized loss function minimization has been shown to…