Computationally feasible estimation of the covariance structure in Generalized linear mixed models(GLMM)

Kenneth Carling (kca@du.se) and Moudud Alam (maa@du.se)
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Kenneth Carling: Department of Business, Economics, Statistics and Informatics, Postal: Örebro University, Department of Business, Economics, Statistics and Informatics, SE - 701 82 ÖREBRO, Sweden
Moudud Alam: Department of Business, Economics, Statistics and Informatics, Postal: Örebro University, Department of Business, Economics, Statistics and Informatics, SE - 701 82 ÖREBRO, Sweden

No 2007:14, Working Papers from Örebro University, School of Business

Abstract: In this paper we discuss how a regression model, with a non-continuous response variable, that allows for dependency between observations should be estimated when observations are clustered and there are repeated measurements on the subjects. The cluster sizes are assumed to be large. We …nd that the conventional estimation technique suggested by the literature on Generalized Linear Mixed Models (GLMM) is slow and often fails due to non-convergence and lack of memory on standard PCs. We suggest to estimate the random e¤ects as …xed e¤ects by GLM and derive the covariance matrix from these estimates. A simulation study shows that our proposal is feasible in terms of Mean-Square Error and computation time. We recommend that our proposal be implemented in the software of GLMM techniques so that the estimation procedure can switch between the conventional technique and our proposal depending on the size of the clusters.

Keywords: Monte-Carlo simulations; large sample; interdependence; cluster error (search for similar items in EconPapers)
JEL-codes: C13 C15 C25 C63 (search for similar items in EconPapers)
Pages: 22 pages
Date: 2007-09-10
New Economics Papers: this item is included in nep-ecm
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