Missing the Random Effect: When the Parameter Space Is Expanding - Couverture souple

Chen, Ru

 
9783639455144: Missing the Random Effect: When the Parameter Space Is Expanding
Couverture souple
ISBN 10 : 3639455142 ISBN 13 : 9783639455144
Editeur : AV Akademikerverlag, 2012

Synopsis

Revision with unchanged content. In this project we look at the case when two of fundamental assumptions in the method of Maximum Likelihood are violated. In particular, we study a special class of misspecified models, where the true model is a mixed effect model but the working model is a fixed effect model with parameters of dimension increasing with sample size. We provide a sufficient condition under which the MLE derived from the working model converges to a welldefined and asymptotically normally-distributed limit. In linear models, the sample variance is biased; but there exists a robust variance estimator of the MLE that converges to the true variance in probability. We also study the Criterion-based automatic model selection methods and find that they may select a linear model that contains spurious variables, but this can be avoided by using the robust variance estimator for the MLE in Bonferroni-adjusted model section or by choosing λn that grows fast enough in Shao’s GIC. In generalized linear models, general results are given and computational and simulation studies are carried out to corroborate asymptotic theoretical results as well as to calculate quantities that are not available in theoretical calculation.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

Revision with unchanged content. In this project we look at the case when two of fundamental assumptions in the method of Maximum Likelihood are violated. In particular, we study a special class of misspecified models, where the true model is a mixed effect model but the working model is a fixed effect model with parameters of dimension increasing with sample size. We provide a sufficient condition under which the MLE derived from the working model converges to a welldefined and asymptotically normally-distributed limit. In linear models, the sample variance is biased; but there exists a robust variance estimator of the MLE that converges to the true variance in probability. We also study the Criterion-based automatic model selection methods and find that they may select a linear model that contains spurious variables, but this can be avoided by using the robust variance estimator for the MLE in Bonferroni-adjusted model section or by choosing λn that grows fast enough in Shao’s GIC. In generalized linear models, general results are given and computational and simulation studies are carried out to corroborate asymptotic theoretical results as well as to calculate quantities that are not available in theoretical calculation.

Biographie de l'auteur

Ph.D in mathematical statistics fromUnviersity of Maryland at College Park.Lead biostatistician at MannkindCorporation, New Jersey.

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

Éditeur
AV Akademikerverlag
Date d'édition
2012
Langue
anglais
ISBN 10 
3639455142
ISBN 13 
9783639455144
Reliure
Broché
Nombre de pages
152
Coordonnées du fabricant
non disponible
Personne responsable
non disponible

Autres éditions populaires du même titre

9783836434577: Missing the Random Effect

Edition présentée

ISBN 10 :  3836434571 ISBN 13 :  9783836434577
Editeur : VDM Verlag Dr. Mueller E.K., 2008
Couverture souple