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Activity Number: 397 - Multiple Aspects of Bayesian Strategies for Variable Selection in Standard and Non-Standard Models
Type: Topic Contributed
Date/Time: Tuesday, July 30, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #301849
Title: Highest Posterior Model Computation and Variable Selection
Author(s): Arnab Kumar Maity* and Sanjib Basu
Companies: Texas A&M University and University of Illinois at Chicago
Keywords: Highest Posterior Model; Variable Selection; Linear Model

Appropriate feature selection is a fundamental problem in the field of statistics. Models with a large number of features or variables require special attention due to the computational complexity of the huge model space. This is generally known as the variable or model selection problem in the field of statistics. The method of variable selection is the process of efficiently selecting an optimal subset of relevant variables for use in model construction. Under the Bayesian approach, a formal way to perform this optimal selection is to select the model with the highest posterior probability. We propose an algorithm to find the highest posterior model with a large number of features. The efficacy of the proposed method will be illustrated via several simulations and real data studies.

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

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