BAYESIAN VARIABLE SELECTION IN LINEAR REGRESSION AND A COMPARISON

Year 2002, Volume: 31, 63 - 76, 01.12.2001

Atilla Yardımcı , Aydın Erar

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.

References

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