576 citations · 1k across the 23 of their papers we have counts for
5 papers · 1 filter
Proximal MCMC for Bayesian Inference of Constrained and Regularized Estimation
Xinkai Zhou, Qiang Heng, Eric C. Chi +1
This paper advocates proximal Markov Chain Monte Carlo (ProxMCMC) as a flexible and general Bayesian inference framework for constrained or regularized estimation. Originally intro…
A Flexible Quasi-Copula Distribution for Statistical Modeling
Sarah S. Ji, Benjamin B. Chu, Hua Zhou +1
Copulas, generalized estimating equations, and generalized linear mixed models promote the analysis of grouped data where non-normal responses are correlated. Unfortunately, parame…
Bayesian Inference using the Proximal Mapping: Uncertainty Quantification under Varying Dimensionality
Maoran Xu, Hua Zhou, Yujie Hu +1
In statistical applications, it is common to encounter parameters supported on a varying or unknown dimensional space. Examples include the fused lasso regression, the matrix recov…
Regularized Matrix Regression
Hua Zhou, Lexin Li
Modern technologies are producing a wealth of data with complex structures. For instance, in two-dimensional digital imaging, flow cytometry, and electroencephalography, matrix typ…
Tensor Regression with Applications in Neuroimaging Data Analysis
Hua Zhou, Lexin Li, Hongtu Zhu
Classical regression methods treat covariates as a vector and estimate a corresponding vector of regression coefficients. Modern applications in medical imaging generate covariates…