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

Hirokazu Yanagihara, Tetsuji Tonda and Chieko Matsumoto

Journal of Multivariate Analysis, 2006, vol. 97, issue 9, 1965-1975

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.

Keywords: Bias; correction; Cross-validation; Predictive; Kullback-Leibler; information; Model; misspecification; Model; selection; Robustness; Weighted; log-likelihood; function (search for similar items in EconPapers)
Date: 2006
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Citations: View citations in EconPapers (1)

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