Dec 4 2019

Statistics and Data Science Seminar: Bayesian high-dimensional logit models: categorical responses and group sparsity, by Seonghyun Jeong

December 4, 2019

4:15 PM - 5:05 PM


636 SEO


Chicago, IL

Seonghyun Jeong (University of Chicago): Bayesian high-dimensional logit models: categorical responses and group sparsity

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.

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Yichao Wu

Date posted

Dec 13, 2019

Date updated

Dec 13, 2019


Seonghyun Jeong | (University of Chicago)