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

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Keyword: Monte-Carlo simulations, Large sample, Interdependence, Cluster errors, Kreditriskmodellering
Publication year: 2007

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 find 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 effects as fixed effects 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.


Kenneth Carling

Högskolan Dalarna; Statistik
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M.M. Alam

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