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Maximized log-likelihood updating and model selection

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  • So, Beong Soo

Abstract

On the basis of simple algebraic identities, we derive a new updating formula for the maximized log-likelihoods. This formula enables us to decompose the predictive minimum description length (PMDL) criterion of Rissanen J. Roy. Statist. Soc. Ser. B 49 (1987) 1 into maximized log-likelihood and a penalty term representing model complexity. Statistical implications for model selection problems are discussed.

Suggested Citation

  • So, Beong Soo, 2003. "Maximized log-likelihood updating and model selection," Statistics & Probability Letters, Elsevier, vol. 64(3), pages 293-303, September.
    • Handle: RePEc:eee:stapro:v:64:y:2003:i:3:p:293-303
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    References listed on IDEAS

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    1. Nishii, R., 1988. "Maximum likelihood principle and model selection when the true model is unspecified," Journal of Multivariate Analysis, Elsevier, vol. 27(2), pages 392-403, November.
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