Missing the Random Effect : When the Parameter Space Is Expanding

Language: English

Published by AV Akademikerverlag, 2012

3639455142 / 9783639455144

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nach der Bestellung gedruckt Neuware - Printed after ordering - 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 n 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.

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Title
Missing the Random Effect : When the Parameter Space Is Expanding
Author
Ru Chen
Publisher
AV Akademikerverlag
Publication year
2012
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
3639455142
ISBN 13
9783639455144
Item weight
244 grams
Dimensions
220x150x10 mm

AHA-BUCH GmbH

Einbeck, Germany

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AbeBooks seller since August 14, 2006

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