BAYESIAN VARIABLE SELECTION IN LINEAR REGRESSION AND A COMPARISON

@inproceedings{Yardmc2006BAYESIANVS,
  title={BAYESIAN VARIABLE SELECTION IN LINEAR REGRESSION AND A COMPARISON},
  author={Atilla Yardımcı and Aydın Erar},
  year={2006}
}
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 β and σ and prior expected levels. 
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