Please use this identifier to cite or link to this item: https://doi.org/10.1093/biomet/asn034
Title: Extended Bayesian information criteria for model selection with large model spaces
Authors: Chen, J.
Chen, Z. 
Keywords: Bayesian paradigm
Consistency
Genome-wide association study
Tournament approach
Variable selection
Issue Date: Sep-2008
Citation: Chen, J., Chen, Z. (2008-09). Extended Bayesian information criteria for model selection with large model spaces. Biometrika 95 (3) : 759-771. ScholarBank@NUS Repository. https://doi.org/10.1093/biomet/asn034
Abstract: The ordinary Bayesian information criterion is too liberal for model selection when the model space is large. In this paper, we re-examine the Bayesian paradigm for model selection and propose an extended family of Bayesian information criteria, which take into account both the number of unknown parameters and the complexity 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 Bayesian information criteria incur a small loss in the positive selection rate but tightly control the false discovery rate, a desirable property in many applications. The extended Bayesian information criteria are extremely useful for variable selection in problems with a moderate sample size but with a huge number of covariates, especially in genome-wide association studies, which are now an active area in genetics research. © 2008 Biometrika Trust.
Source Title: Biometrika
URI: http://scholarbank.nus.edu.sg/handle/10635/105146
ISSN: 00063444
DOI: 10.1093/biomet/asn034
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