title: Bootstrap choice of estimators in parametric and semiparametric families: an extension of EIC.
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.
authors.affiliation: INSERM U330, 146 rue Lèo Saignat, 33076 Bordeaux, France. benoit.liquet@isped.u-bordeaux2.fr
authors.forename: B
authors.initials: B
authors.lastname: Liquet
authors.forename: C
authors.initials: C
authors.lastname: Sakarovitch
authors.forename: D
authors.initials: D
authors.lastname: Commenges
mesh_terms: D000367:Age Factors; D003198:Computer Simulation; D015658:HIV Infections / Q000635:transmission; D015497:HIV-1 / Q000378:metabolism; D006801:Humans; D007223:Infant; D016013:Likelihood Functions; D012367:RNA, Viral / Q000032:analysis; D012044:Regression Analysis; D018401:Sample Size
chemical_list: D012367:RNA, Viral