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Bias correction of cross-validation criterion based on Kullback–Leibler information under a general condition by Hirokazu Yanagihara; Tetsuji Tonda; Chieko Matsumoto is a Mathematics article available to read on EtoBox.

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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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Author

Hirokazu Yanagihara; Tetsuji Tonda; Chieko Matsumoto

Publisher

Elsevier Science; Elsevier ; Elsevier Inc.; Elsevier BV (ISSN 0047-259X)

Published

2006

Language

EN

Field

Mathematics (Physical Sciences)