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Activity Number: 512 - Bayesian Model Selection
Type: Contributed
Date/Time: Wednesday, August 2, 2017 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #324206 View Presentation
Title: Bayesian High-Dimensional Linear Regression Under the Model Misspecification
Author(s): Kyoungjae Lee* and Minwoo Chae and Lizhen Lin
Companies: The University of Notre Dame and The University of Texas at Austin and The University of Notre Dame
Keywords: Posterior convergence rate ; High-dimensional semiparametric model ; Model misspecification
Abstract:

In a high-dimensional linear regression model, a Gaussian error distribution has been frequently used because of its theoretical and computational conveniences. However, it may face some serious problems, such as overfitting and inaccurate uncertainty quantification, when the true error distribution is not a Gaussian distribution. We study asymptotic properties of a Bayesian linear regression model possibly with an unknown error distribution. A mixture prior distribution is adopted to fit the unknown error distribution, and properties of the posterior such as the convergence rate of the regression coefficient and the model selection consistency are studied under certain conditions.


Authors who are presenting talks have a * after their name.

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