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Thursday, May 17
Bayesian Modeling
Thu, May 17, 3:00 PM - 3:45 PM
Regency Ballroom B
 

Choosing Among a Class of Zellner’s g-Priors in Bayesian Regression Models and Subset Selection of Variables Using the Genetic Algorithm and Information Complexity (304474)

Hamparsum Bozdogan, University of Tennessee 
*Yaojin Sun, The University of Tennessee 

Keywords: Zellner’s g-priors, Bayesian Regression, Subset Selection, Genetic Algorithm, Entopic Complexity, and ICOMP.

This e-poster introduces and develops Bayesian regression models under a class of celebrated Zellner’s g-priors for subset selection of best predictors using the genetic algorithm (GA) and information complexity (ICOMP) criterion as our fitness function. We introduce a new entropic complexity based g-priors to score in addition to the existing g-priors in the literature for subset selection. We perform a large scale Monte Carlo simulation study under different g-priors in Bayesian regression models to choose the best g-prior among the candidate g-priors including the entropic complexity based g-priors in subset selection of variables. Our results show the performance of each g-prior. We also show our results on a benchmark data set to demonstrate the utility and versatility of our proposed approach using the GA and ICOMP as the fitness function. The results obtained show that entropic complexity based g-priors outperform currently used existing g-priors in the Bayesian regression models.