Bootstrap choice of estimators in parametric and semiparametric families: an extension of EIC |
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Authors: | Liquet B Sakarovitch C Commenges D |
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Institution: | INSERM U330, 146 rue Lèo Saignat, 33076 Bordeaux, France. benoit.liquet@isped.u-bordeaux2.fr |
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Abstract: | Ishiguro, Sakamoto, and Kitagawa (1997, Annals of the Institute of Statistical Mathematics 49, 411-434) proposed EIC as an extension of Akaike criterion (AIC); the idea leading to EIC is to correct the bias of the log-likelihood, considered as an estimator of the Kullback-Leibler information, using bootstrap. We develop this criterion for its use in multivariate semiparametric situations, and argue that it can be used for choosing among parametric and semiparametric estimators. A simulation study based on aregression model shows that EIC is better than its competitors although likelihood cross-validation performs nearly as well except for small sample size. Its use is illustrated by estimating the mean evolution of viral RNA levels in a group of infants infected by HIV. |
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Keywords: | Bootstrap Kullback-Leibler information Regression Semiparametric Smoothing |
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