Resampling for order estimation of autoregressive models with missing data

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AuteurDJIBRIL MOUSSA, FREEDATH LAYE ERI YOMI
AuteurEL MATOUAT, ABDELAZIZ
AuteurHAMZAOUI, HASSANIA
Date d'ajout2026-06-02T16:06:57Z
Date de disponibilite2026-06-02T16:06:57Z
Date de publication2015
ResumeIn this artticle, we consider the order estimation of autoregressive modelswith incomplete data using expectation maximization(EM) algorithm based information criteria.The criteria take the form of a penalization of the conditionnal expectation of the log-likelihood. The ealuation of the penalization term generally involves numerical differenciation and matrix inversion. We introduce a simplification of the penalization term for autoregressive model selection and we propose a penalty factor based on a resampling procedure in the criteria formula. The simulation results show the improvement yielded by the proposed method when compares to the classical information criteria for model selection with incomplete data.
DOI10.1080/03610918.2013.809189
Autre identifiantBECDB-2956
URIhttps://dspace.uac.bj/handle/123456789/2947
Languefr
Fait partie deCommunications in Statistics Simulation and computation
Sujetautoregressive model
SujetEM algorithm
SujetInformation criteria
SujetMissing data
Sujetresampling
TitreResampling for order estimation of autoregressive models with missing data
TypeArticle

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