STATISTICS COLLOQUIUM
Bani K. Mallick
Susan M. Arseven `75 Chair in Data Science and
Computational Statistics
University Distinguished Professor
Director, Center for Statistical Bioinformatics
Director, Bayesian Bioinformatics Laboratory
Texas A & M University
Bayesian Gaussian Graphical Models and their extensions
Abstract
Gaussian graphical models (GGMs) are well-established tools for probabilistic exploration of dependence structures using precision (inverse covariance) matrices. We propose a Bayesian method for estimating the precision matrix in GGMs. The method leads to a sparse and adaptively shrunk estimator of the precision matrix, and thus conduct model selection and estimation simultaneously. We extend this method in a regression setup with the presence of covariates. We consider both the linear as well as the nonlinear regressions in this GGM framework. Furthermore, to relax the assumption of the Gaussian distribution, we develop a quantile based approach for sparse estimation of graphs. We demonstrate that the resulting graph estimator is robust to outliers and applicable under general distributional assumptions. We discuss a few applications of the proposed models.
DATE: Wednesday, April 19, 2017
TIME: 4:00 pm
PLACE: Philip E. Austin Bldg., Rm. 105
Coffee will be served at 3:30 pm in the Noether Lounge (AUST 326)
For more information, contact: Tracy Burke at tracy.burke@uconn.edu