Bayesian variable selection using an adaptive powered correlation prior
Arun Krishna, Howard D Bondell, Sujit K Ghosh
JOURNAL OF STATISTICAL PLANNING AND INFERENCE | ELSEVIER SCIENCE BV | Published : 2009
The problem of selecting the correct subset of predictors within a linear model has received much attention in recent literature. Within the Bayesian framework, a popular choice of prior has been Zellner's g-prior which is based on the inverse of empirical covariance matrix of the predictors. An extension of the Zellner's prior is proposed in this article which allow for a power parameter on the empirical covariance of the predictors. The power parameter helps control the degree to which correlated predictors are smoothed towards or away from one another. In addition, the empirical covariance of the predictors is used to obtain suitable priors over model space. In this manner, the power para..View full abstract
Awarded by NSF
Awarded by NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES
The authors Would like to thank the executive editor and the three anonymous referees for their useful comments which has lead to an improved version of an earlier manuscript. We Would also like to thank Dr. Brian Reich in the Department of Statistics at North Carolina State University for his helpful comments and Stimulating discussions. H. Bondell's research was partially supported by NSF Grant no. DMS-0705968.