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Some connections between Bayesian and non-Bayesian methods for regression model selection

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  • Liang, Faming

Abstract

In this article, we study the connections between Bayesian methods and non-Bayesian methods for variable selection in multiple linear regression. We show that each of the non-Bayesian criteria, FPE[alpha], AIC, Cp and adjusted , has its Bayesian correspondence under an appropriate prior setting. The theoretical results are illustrated by numerical simulations.

Suggested Citation

  • Liang, Faming, 2002. "Some connections between Bayesian and non-Bayesian methods for regression model selection," Statistics & Probability Letters, Elsevier, vol. 57(1), pages 53-63, March.
    • Handle: RePEc:eee:stapro:v:57:y:2002:i:1:p:53-63
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    References listed on IDEAS

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    1. Hirotugu Akaike, 1969. "Fitting autoregressive models for prediction," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 21(1), pages 243-247, December.
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    Cited by:

    1. Qifan Song & Faming Liang, 2015. "High-Dimensional Variable Selection With Reciprocal L 1 -Regularization," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1607-1620, December.

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