Seonghyun Jeong : Bayesian high-dimensional logit models: categorical responses and group sparsity

Posted by Yichao Wu , part of the Statistics and Data Science Seminar.

At

Dec. 4, 2019, 4:15 p.m.

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636 SEO

Abstract

This study investigates frequentist properties of Bayesian high-dimensional logit models for categorical response variables. For high-dimensional regression coefficients, group sparse modeling is adopted to handle model selection with categorical responses. A product of a point mass and a Laplace-type distribution is used for the prior distribution on sparse regression coefficients. The procedure exhibits nearly optimal posterior contraction. A shape approximation to the posterior distribution is characterized to show model selection consistency. The distributional approximation also leads to a Bernstein-von Mises theorem for uncertainty quantification through credible sets with guaranteed frequentist coverage.

Note the unusual time.