55 citations · 87 across the 11 of their papers we have counts for
6 papers · 1 filter
Penalized Sparse Covariance Regression with High Dimensional Covariates
Yuan Gao, Zhiyuan Zhang, Zhanrui Cai +3
Covariance regression offers an effective way to model the large covariance matrix with the auxiliary similarity matrices. In this work, we propose a sparse covariance regression (…
A Latent Factor Model for High-Dimensional Binary Data
Jiaxin Shi, Yuan Gao, Rui Pan +1
In this study, we develop a latent factor model for analysing high-dimensional binary data. Specifically, a standard probit model is used to describe the regression relationship be…
A Selective Review on Statistical Methods for Massive Data Computation: Distributed Computing, Subsampling, and Minibatch Techniques
Xuetong Li, Yuan Gao, Hong Chang +11
This paper presents a selective review of statistical computation methods for massive data analysis. A huge amount of statistical methods for massive data computation have been rap…
Mixture Conditional Regression with Ultrahigh Dimensional Text Data for Estimating Extralegal Factor Effects
Jiaxin Shi, Fang Wang, Yuan Gao +2
Testing judicial impartiality is a problem of fundamental importance in empirical legal studies, for which standard regression methods have been popularly used to estimate the extr…
Testing Sufficiency for Transfer Learning
Ziqian Lin, Yuan Gao, Feifei Wang +1
Modern statistical analysis often encounters high dimensional models but with limited sample sizes. This makes the target data based statistical estimation very difficult. Then how…
An Asymptotic Analysis of Minibatch-Based Momentum Methods for Linear Regression Models
Yuan Gao, Xuening Zhu, Haobo Qi +3
Momentum methods have been shown to accelerate the convergence of the standard gradient descent algorithm in practice and theory. In particular, the minibatch-based gradient descen…