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Bootstrap Choice of Estimators in Parametric and Semiparametric Families: An Extension of EIC
Author(s) -
Liquet B.,
Sakarovitch C.,
Commenges D.
Publication year - 2003
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/1541-0420.00020
Subject(s) - akaike information criterion , estimator , mathematics , statistics , extension (predicate logic) , semiparametric regression , parametric statistics , semiparametric model , econometrics , multivariate statistics , computer science , programming language
Summary . 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 a regression 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.