School of Mathematics and Statistical Sciences Colloquium talk by Min Wang

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Title of the talk: Low‐Rank Variational Correction Estimation for Multi‐Source Heterogeneous Quantile Linear Regression Models

Abstract: High-dimensional data arising in genomics, econometrics, and clinical medicine often exhibit substantial heterogeneity across multiple sources. While existing methods address multi-source heterogeneity, they do not adequately accommodate the combined challenges of high dimensionality and between-source heterogeneity. To address this gap, we propose a scalable Bayesian framework for multi-source heterogeneous quantile regression with spike-and-slab priors for simultaneous parameter estimation and feature selection. To overcome computational challenges, we combine mean-field variational inference with Laplace approximation and introduce a novel low-rank variational correction strategy that substantially improves approximation accuracy and adaptability in high-dimensional heterogeneous settings. This low-rank correction effectively captures the underlying dependence structure, leading to more robust and efficient inference. For model assessment and diagnostic analysis, we further develop a Bayesian score test coupled with local influence analysis. Extensive simulation studies and an analysis of The Cancer Genome Atlas (TCGA) data from four cancer cohorts (ESCA, PAAD, PCPG, and READ) demonstrate the computational efficiency, scalability, and practical utility of the proposed method in high-dimensional heterogeneous applications. The proposed low-rank variational correction algorithms are implemented in the R package LRQVB, which is publicly available on CRAN.

Short bio of the speaker: Min Wang is a professor and the inaugural chair of the Department of Statistics and Data Science in the College of AI, Cyber and Computing at The University of Texas at San Antonio. He previously worked at Michigan Technological University and Texas Tech University. His main research spans the areas of Bayesian inference and methods, high-dimensional inference, prior elicitation, quantile regression, and statistical modeling, all in both methodological and theoretical perspectives. His recent work has been appeared or accepted in top-ranked journals, such as Bayesian Analysis, Bernoulli, IISE Transactions, Statistics in Medicine, European Journal of Operational Research, Journal of Statistical Planning and Inference, Naval Research Logistics, TEST, The American Statistician, and others.