BAYESIAN VARIABLE SELECTION IN LINEAR REGRESSION AND A COMPARISON

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Hacettepe Journal of Mathematics and Statistics
Volume:31
BAYESIAN VARIABLE SELECTION IN LINEAR REGRESSION AND A COMPARISON

BAYESIAN VARIABLE SELECTION IN LINEAR REGRESSION AND A COMPARISON

Authors : Atilla YARDIMCI, Aydın ERAR

Pages : 63-76

View : 71 | Download : 11

Publication Date : 2001-12-01

Article Type : Research Paper

Abstract :In this study, Bayesian approaches, such as Zellner, Occam`s Window and Gibbs sampling, have been compared in terms of selecting the correct subset for the variable selection in a linear regression model. The aim of this comparison is to analyze Bayesian variable selection and the behavior of classical criteria by taking into consideration the different values of $\beta$ and $\sigma$ and prior expected levels.
Keywords : Bayesian variable selection, Prior distribution, Gibbs Sampling, Markov Chain Monte Carlo

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