@techreport{oai:tsukuba.repo.nii.ac.jp:00028287,
 author = {KABE, Satoshi and KANAZAWA, Yuichiro and 金澤, 雄一郎},
 month = {Feb},
 note = {In Bayesian data analysis, a deviance information criterion (DIC)
proposed by Spiegelhalter et al. (2002) is widely used for the model
selection, since this criterion is relatively easy to calculate and applicable
to a wide range of statistical models. Spiegelhalter et al. (2002)
gave an asymptotic justification of DIC in the case where the number
of observations grows with respect to the number of parameters.
In small-sample cases, however, the estimated asymptotic bias of DIC
might underestimate the true bias (Burnham, 2002). In this paper, we
propose a finite-sample bias corrected information criterion (ICBL) for
the Bayesian linear regression models with conjugate priors, as AICC
proposed by Sugiura (1978) in frequentist framework. We examine the
performance of the proposed information criterion relative to the DIC
for small-sample cases by simulation, and found that our proposed
information criterion outperforms DIC.},
 title = {Variable Selection for Bayesian Linear Regression Model in a Finite Sample Size},
 year = {2013}
}