Consistency of information criteria for model selection with missing data

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AuteurEL MATOUAT, ABDELAZIZ
AuteurDJIBRIL MOUSSA, FREEDATH LAYE ERI YOMI
AuteurHAMZAO, HASSANIA
Date d'ajout2026-06-02T16:06:57Z
Date de disponibilite2026-06-02T16:06:57Z
Date de publication2016
ResumeIn this paper, we investigate the consistency of the Expectation Maximization (EM) algorithm-based information criteria for model selection with missing data. The criteria correspond to a penalization of the conditional expectation of the complete data log-likelihood given the observed data and with respect to the missing data conditional density. We present asymptotic properties related to maximum likelihood estimation in the presence of incomplete data and we provide sufficient conditions for the consistency of model selection by minimizing the information criteria. Their finite sample performance is illustrated through simulation and real data studies.
DOI10.1080/03610926.2014.972568
Autre identifiantBECDB-14913
URIhttps://dspace.uac.bj/handle/123456789/12681
Languefr
Fait partie deCommunications in Statistics - Theory and Methods
Sujetconsistency
SujetEM algorithm
SujetInformation criteria
SujetKullback–Leibler divergence
SujetMissing data
TitreConsistency of information criteria for model selection with missing data
TypeArticle

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