Review Articles

A discussion of prior-based Bayesian information criterion (PBIC)

Jiming Jiang ,

Department of Statistics, University of California, Davis, Davis, CA, USA; Department of Public Health and Preventive Medicine, Oregon Health and Science University, Portland, OR, USA

jimjiang@ucdavis.edu

Thuan Nguyen

Department of Statistics, University of California, Davis, Davis, CA, USA; Department of Public Health and Preventive Medicine, Oregon Health and Science University, Portland, OR, USA

Pages 17-18 | Received 12 Jan. 2019, Accepted 13 Jan. 2019, Published online: 13 Mar. 2019,
  • Abstract
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"A discussion of prior-based Bayesian information criterion (PBIC)." Statistical Theory and Related Fields, 3(1), pp. 17–18

Disclosure statement

No potential conflict of interest was reported by the authors.

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References

  1. Broman, K. W., & Speed, T. P. (2002). A model selection approach for the identification of quantitative trait loci in experiemental crosses. Journal of the Royal Statistical Society, Series B64, 641–656. doi: 10.1111/1467-9868.00354 [Crossref], [Google Scholar]
  2. Chen, J., & Chen, Z. (2008). Extended Bayesian information criteria for model selection with large model spaces. Biometrika95, 759–771. doi: 10.1093/biomet/asn034 [Crossref][Web of Science ®], [Google Scholar]
  3. Fan, J., & Li, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association96, 1348–1360. doi: 10.1198/016214501753382273 [Taylor & Francis Online][Web of Science ®], [Google Scholar]
  4. Hartley, H. O., & Rao, J. N. K. (1967). Maximum likelihood estimation for the mixed analysis of variance model. Biometrika54, 93–108. doi: 10.1093/biomet/54.1-2.93 [Crossref][Web of Science ®], [Google Scholar]
  5. Harville, D. A. (1977). maximum likelihood approaches to variance components estimation and related problems. Journal of the American Statistical Association72, 320–340. doi: 10.1080/01621459.1977.10480998 [Taylor & Francis Online][Web of Science ®], [Google Scholar]
  6. Jiang, J., & Nguyen, T. (2015). The fence methods. Singapore: World Scientific. [Crossref], [Google Scholar]
  7. Jiang, J., Rao, J. S., Gu, Z., & Nguyen, T. (2008). Fence methods for mixed model selection. The Annals of Statistics36, 1669–1692. doi: 10.1214/07-AOS517 [Crossref][Web of Science ®], [Google Scholar]
  8. Miller, J. J. (1977). Asymptotic properties of maximum likelihood estimates in the mixed model of analysis of variance. The Annals of Statistics5, 746–762. doi: 10.1214/aos/1176343897 [Crossref][Web of Science ®], [Google Scholar]
  9. Müller, S., Scealy, J. L., & Welsh, A. H. (2013). Model selection in linear mixed models. Statistical Science28, 135–167. doi: 10.1214/12-STS410 [Crossref][Web of Science ®], [Google Scholar]
  10. Ye, J. (1998). On measuring and correcting the effects of data mining and model selection. Journal of the American Statistical Association93, 120–131. doi: 10.1080/01621459.1998.10474094 [Taylor & Francis Online][Web of Science ®], [Google Scholar]