BAYESIAN VARIABLE SELECTION IN LINEAR REGRESSION AND A COMPARISON

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

Atilla Yardımcı , Aydın Erar

https://izlik.org/JA77EK82KN

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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