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

Location

636 SEO

Address

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.

Note the unusual time.

Please click here to make changes to, or delete, this seminar announcement.

Contact

Yichao Wu

Date posted

Dec 13, 2019

Date updated

Dec 13, 2019

Speakers

Seonghyun Jeong | (University of Chicago)