Extended Bayesian information criteria for model selection with large model spaces

The ordinary Bayes information criterion is too liberal for model selection when the model space is large. In this article, we re-examine the Bayesian paradigm for model selection and propose an extended family of Bayes information criteria. The new criteria take into account both the number of unknown parameters and the com-plexity of the model space. Their consistency is established, in particular allowing the number of covariates to increase to infinity with the sample size. Their performance in various situations is evaluated by simulation studies. It is demonstrated that the extended Bayes information criteria incur a small loss in the positive selection rate but tightly control the false discovery rate,…

• NU
  National University of Singapore
• UO
  University of British Columbia
• NS
  Natural Sciences and Engineering Research Council of Canada