576 citations · 1k across the 23 of their papers we have counts for
23 papers
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 Trend Filtering via Proximal Markov Chain Monte Carlo
Qiang Heng, Hua Zhou, Eric C. Chi
Proximal Markov Chain Monte Carlo is a novel construct that lies at the intersection of Bayesian computation and convex optimization, which helped popularize the use of nondifferen…
Orthogonal Trace-Sum Maximization: Tightness of the Semidefinite Relaxation and Guarantee of Locally Optimal Solutions
Joong-Ho Won, Teng Zhang, Hua Zhou
This paper studies an optimization problem on the sum of traces of matrix quadratic forms in semi-orthogonal matrices, which can be considered as a generalization of the synchr…
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…
Nonconvex Optimization via MM Algorithms: Convergence Theory
Kenneth Lange, Joong-Ho Won, Alfonso Landeros +1
The majorization-minimization (MM) principle is an extremely general framework for deriving optimization algorithms. It includes the expectation-maximization (EM) algorithm, proxim…
OPENMENDEL: A Cooperative Programming Project for Statistical Genetics
Hua Zhou, Janet S. Sinsheimer, Christopher A. German +12
Statistical methods for genomewide association studies (GWAS) continue to improve. However, the increasing volume and variety of genetic and genomic data make computational speed a…
Orthogonal Trace-Sum Maximization: Applications, Local Algorithms, and Global Optimality
Joong-Ho Won, Hua Zhou, Kenneth Lange
This paper studies the problem of maximizing the sum of traces of matrix quadratic forms on a product of Stiefel manifolds. This orthogonal trace-sum maximization (OTSM) problem ge…
Proximal Distance Algorithms: Theory and Examples
Kevin L. Keys, Hua Zhou, Kenneth Lange
Proximal distance algorithms combine the classical penalty method of constrained minimization with distance majorization. If is the loss function, and is th…
MM Algorithms for Variance Components Models
Hua Zhou, Liuyi Hu, Jin Zhou +1
Variance components estimation and mixed model analysis are central themes in statistics with applications in numerous scientific disciplines. Despite the best efforts of generatio…
Fast Genome-Wide QTL Analysis Using Mendel
Hua Zhou, Jin Zhou, Tao Hu +2
Pedigree GWAS (Option 29) in the current version of the Mendel software is an optimized subroutine for performing large scale genome-wide QTL analysis. This analysis (a) works for…
Fast Genome-Wide QTL Association Mapping on Pedigree and Population Data
Hua Zhou, John Blangero, Thomas D. Dyer +3
Since most analysis software for genome-wide association studies (GWAS) currently exploit only unrelated individuals, there is a need for efficient applications that can handle gen…
IsoDOT Detects Differential RNA-isoform Expression/Usage with respect to a Categorical or Continuous Covariate with High Sensitivity and Specificity
Wei Sun, Yufeng Liu, James J. Crowley +14
We have developed a statistical method named IsoDOT to assess differential isoform expression (DIE) and differential isoform usage (DIU) using RNA-seq data. Here isoform usage refe…
Distance Majorization and Its Applications
Eric C. Chi, Hua Zhou, Kenneth Lange
The problem of minimizing a continuously differentiable convex function over an intersection of closed convex sets is ubiquitous in applied mathematics. It is particularly interest…
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…
Path Following in the Exact Penalty Method of Convex Programming
Hua Zhou, Kenneth Lange
Classical penalty methods solve a sequence of unconstrained problems that put greater and greater stress on meeting the constraints. In the limit as the penalty constant tends to $…
A Generic Path Algorithm for Regularized Statistical Estimation
Hua Zhou, Yichao Wu
Regularization is widely used in statistics and machine learning to prevent overfitting and gear solution towards prior information. In general, a regularized estimation problem mi…
Path Following and Empirical Bayes Model Selection for Sparse Regression
Hua Zhou, Artin Armagan, David B. Dunson
In recent years, a rich variety of regularization procedures have been proposed for high dimensional regression problems. However, tuning parameter choice and computational efficie…
A Path Algorithm for Constrained Estimation
Hua Zhou, Kenneth Lange
Many least squares problems involve affine equality and inequality constraints. Although there are variety of methods for solving such problems, most statisticians find constrained…
MM Algorithms for Geometric and Signomial Programming
Kenneth Lange, Hua Zhou
This paper derives new algorithms for signomial programming, a generalization of geometric programming. The algorithms are based on a generic principle for optimization called the…
Graphics Processing Units and High-Dimensional Optimization
Hua Zhou, Kenneth Lange, Marc A. Suchard
This paper discusses the potential of graphics processing units (GPUs) in high-dimensional optimization problems. A single GPU card with hundreds of arithmetic cores can be inserte…
Rates of convergence of some multivariate Markov chains with polynomial eigenfunctions
Kshitij Khare, Hua Zhou
We provide a sharp nonasymptotic analysis of the rates of convergence for some standard multivariate Markov chains using spectral techniques. All chains under consideration have mu…