- Hacettepe Journal of Mathematics and Statistics
- Vol: 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 : 10 | Download : 3
Publication Date : 2001-12-01
Article Type : Research
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.