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Power-expected-posterior prior Bayes factor consistency for nested linear models with increasing dimensions

Author

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  • D. Fouskakis
  • J. K. Innocent
  • L. Pericchi

Abstract

The power-expected-posterior prior is used in this paper for comparing nested linear models. The asymptotic behaviour of the method is investigated for different values of the power parameter of the prior. Focus is given on the consistency of the Bayes factor of comparing the full model $M_p $Mp versus a generic submodel $M_\ell $Mℓ. In each case, we allow the true generating model to be either $M_p $Mp or $M_\ell $Mℓ and we keep the dimension of $M_\ell $Mℓ fixed, while the dimension of $M_p $Mp can be either fixed or (grow as) $O(n) $O(n), with n denoting the sample size.

Suggested Citation

  • D. Fouskakis & J. K. Innocent & L. Pericchi, 2020. "Power-expected-posterior prior Bayes factor consistency for nested linear models with increasing dimensions," Statistical Theory and Related Fields, Taylor & Francis Journals, vol. 4(2), pages 162-171, July.
    • Handle: RePEc:taf:tstfxx:v:4:y:2020:i:2:p:162-171
    DOI: 10.1080/24754269.2020.1719355
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