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Computationally feasible estimation of the covariance structure in generalized linear mixed models 
Örebro University, Swedish Business School at Örebro University.
Dalarna University, SE 781 88 Borlange, Sweden.
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
London: Taylor & Francis , 2008. Vol. 78, no 12, p. 1229-1239
Keywords [en]
Monte Carlo simulations, Large sample, Interdependence, Cluster errors
National Category
Probability Theory and Statistics Social Sciences
Research subject
Statistics
Identifiers
URN: urn:nbn:se:oru:diva-14060DOI: 10.1080/00949650701688547OAI: oai:DiVA.org:oru-14060DiVA, id: diva2:389258
Note
Mr Alam is also affiliated to Dalarna University, SE 781 88 Borlange, SwedenAvailable from: 2011-01-19 Created: 2011-01-19 Last updated: 2017-12-11Bibliographically approved
In thesis
1. Feasible computation of generalized linear mixed models with application to credit risk modelling
Open this publication in new window or tab >>Feasible computation of generalized linear mixed models with application to credit risk modelling
2010 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis deals with developing and testing feasible computational procedures to facilitate the estimation of and carry out the prediction with the generalized linear mixed model (GLMM) with a scope of applying them to large data sets. The work of this thesis is motivated from an issue arising incredit risk modelling. We have access to a huge data set, consisting of about one million observations, on credit history obtained from two major Swedish banks. The principal research interest involved with the data analysis is to model the probability of credit defaults by incorporating the systematic dependencies among the default events. In order to model the dependent credit defaults we adopt the framework of GLMM which is apopular approach to model correlated binary data. However, existing computational procedures for GLMM did not offer us the flexibility to incorporate the desired correlation structure of defaults events.For the feasible estimation of the GLMM we propose two estimation techniques being the fixed effects (FE) approach and the two-step pseudolikelihood approach (2PL). The preciseness of the estimation techniques and their computational advantages are studied by Monte-Carlo simulations and by applying them to the credit risk modelling. Regarding the prediction issue, we show how to apply the likelihood principle to carryout prediction with GLMM. We also provide an R add-in package to facilitate the predictive inference for GLMM.

Place, publisher, year, edition, pages
Örebro: Örebro universitet, 2010. p. 29
Series
Örebro Studies in Statistics, ISSN 1651-8608 ; 5
Keywords
Credit risk, cluster correlation, GLMM, large data, two-step pseudo likelihood estimation, defaults contagion, predictive likelihood
National Category
Social Sciences Probability Theory and Statistics
Research subject
Statistics
Identifiers
urn:nbn:se:oru:diva-12390 (URN)978-91-7668-771-0 (ISBN)
Public defence
2010-12-21, Hörsal M, Örebro universitet, Fakultetsgatan 1, 701 82 Örebro, 15:15 (English)
Opponent
Available from: 2010-11-04 Created: 2010-11-02 Last updated: 2017-10-17Bibliographically approved

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Publisher's full texthttp://dx.doi.org/10.1080/00949650701688547

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Alam, Md. Moudud

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