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Computionally feasible estimation of the covariance structure in generalized linear mixed models
Högskolan Dalarna, Akademin Industri och samhälle, Statistik.ORCID-id: 0000-0002-3183-3756
Högskolan Dalarna, Akademin Industri och samhälle, Statistik.ORCID-id: 0000-0003-2317-9157
2008 (Engelska)Ingår i: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 78, nr 12, s. 1229-1239Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
2008. Vol. 78, nr 12, s. 1229-1239
Nyckelord [en]
Monte Carlo simulations; large samples; interdependence; cluster errors
Nationell ämneskategori
Sannolikhetsteori och statistik
Forskningsämne
Komplexa system - mikrodataanalys, Kreditriskmodellering
Identifikatorer
URN: urn:nbn:se:du-3669DOI: 10.1080/00949650701688547ISI: 000260497300008OAI: oai:dalea.du.se:3669DiVA, id: diva2:519999
Tillgänglig från: 2009-01-30 Skapad: 2009-01-30 Senast uppdaterad: 2017-12-07Bibliografiskt granskad

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Alam, MoududCarling, Kenneth

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