Some Model Selection Criteria based on Incomplete or Complete data - Couverture souple

Hafidi, Bezza

 
9786202281096: Some Model Selection Criteria based on Incomplete or Complete data
Couverture souple
ISBN 10 : 620228109X ISBN 13 : 9786202281096
Editeur : Éditions universitaires européennes, 2018

Synopsis

This book present some extensions of model selection criteria based on incomplete or complete data. First, we have derived a corrected version of the KIC criterion, based on the Kullback's symmetric divergence for multiple and multivariate regression models and for univariate and multivariate autoregressive models. Its signal-to-noise ratios is pointed out in each case. In the presence of missing data, we derived and investigated a variant of KIC criterion. We examine the performance of the new criterion relative to other well known criteria in a large simulation study. Also, we present a variants of a Schwarz information criterion for model selection in the settings where the observed-data is incomplete. The performance of these criteria, relative to other well known criteria, is examined in a large simulation study. Finally, we study the asymptotic property and the performance of the repeated half sampling (RHS) criterion. Two cases are distinguish for the true model, either the candidate family of models does include the true model or does not include it. The performance of RHS criterion is compared with other criteria in a simulation study.

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Présentation de l'éditeur

This book present some extensions of model selection criteria based on incomplete or complete data. First, we have derived a corrected version of the KIC criterion, based on the Kullback's symmetric divergence for multiple and multivariate regression models and for univariate and multivariate autoregressive models. Its signal-to-noise ratios is pointed out in each case. In the presence of missing data, we derived and investigated a variant of KIC criterion. We examine the performance of the new criterion relative to other well known criteria in a large simulation study. Also, we present a variants of a Schwarz information criterion for model selection in the settings where the observed-data is incomplete. The performance of these criteria, relative to other well known criteria, is examined in a large simulation study. Finally, we study the asymptotic property and the performance of the repeated half sampling (RHS) criterion. Two cases are distinguish for the true model, either the candidate family of models does include the true model or does not include it. The performance of RHS criterion is compared with other criteria in a simulation study.

Biographie de l'auteur

Bezza Hafidi Professor of Higher Education, Ability to direct research in Statistics. Ibn Zohr University, Faculty of Sciences Agadir.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
Éditions universitaires européennes
Date d'édition
2018
Langue
anglais
ISBN 10 
620228109X
ISBN 13 
9786202281096
Reliure
Broché
Nombre de pages
108
Coordonnées du fabricant
non disponible
Personne responsable
Andreas Meier
22029659400
info@rheinberg-buch.de

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