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Bias correction of cross-validation criterion based on Kullback-Leibler information under a general condition

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  • Yanagihara, Hirokazu
  • Tonda, Tetsuji
  • Matsumoto, Chieko
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    Abstract

    This paper deals with the bias correction of the cross-validation (CV) criterion to estimate the predictive Kullback-Leibler information. A bias-corrected CV criterion is proposed by replacing the ordinary maximum likelihood estimator with the maximizer of the adjusted log-likelihood function. The adjustment is just slight and simple, but the improvement of the bias is remarkable. The bias of the ordinary CV criterion is O(n-1), but that of the bias-corrected CV criterion is O(n-2). We verify that our criterion has smaller bias than the AIC, TIC, EIC and the ordinary CV criterion by numerical experiments.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 97 (2006)
    Issue (Month): 9 (October)
    Pages: 1965-1975

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    Handle: RePEc:eee:jmvana:v:97:y:2006:i:9:p:1965-1975

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    Related research

    Keywords: Bias correction Cross-validation Predictive Kullback-Leibler information Model misspecification Model selection Robustness Weighted log-likelihood function;

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