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Computionally feasible estimation of the covariance structure in generalized linear mixed models
Dalarna University, School of Technology and Business Studies, Statistics.ORCID iD: 0000-0002-3183-3756
Dalarna University, School of Technology and Business Studies, Statistics.ORCID iD: 0000-0003-2317-9157
2008 (English)In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 78, no 12, p. 1229-1239Article in journal (Refereed) Published
Abstract [en]

In this paper, we discuss how a regression model, with a non-continuous response variable, which allows for dependency between observations, should be estimated when observations are clustered and measurements on the subjects are repeated. 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 sometimes fails due to non-convergence and lack of memory on standard PCs. We suggest to estimate the random effects as fixed effects by generalized linear model and to 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.

Place, publisher, year, edition, pages
2008. Vol. 78, no 12, p. 1229-1239
Keywords [en]
Monte Carlo simulations; large samples; interdependence; cluster errors
National Category
Probability Theory and Statistics
Research subject
Complex Systems – Microdata Analysis, Kreditriskmodellering
Identifiers
URN: urn:nbn:se:du-3669DOI: 10.1080/00949650701688547ISI: 000260497300008OAI: oai:dalea.du.se:3669DiVA, id: diva2:519999
Available from: 2009-01-30 Created: 2009-01-30 Last updated: 2017-12-07Bibliographically approved

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Alam, MoududCarling, Kenneth

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  • de-DE
  • en-GB
  • en-US
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
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