Feasible computation of the generalized linear mixed ... › smash › get › diva2:360110 ›...

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Md. Moudud Alam Feasible computation of the generalized linear mixed models ... ÖREBRO STUDIES IN STATISTICS 5 2010 issn 1651-8608 isbn 978-91-7668-771-0 Md. Moudud Alam (b. 1976) is a doctoral student in Statistics at Dalarna University. His main research interest is the generalized linear models, and computational statistics and their applications in economic and social research. The thesis presents feasible computational procedures to estimate of and carry out prediction with the genera- lized linear mixed model (GLMM) for large data sets. This thesis is motivated from an issue of modelling the probability of credit defaults with a large data set obtained from two major Swedish banks. To model the dependent credit defaults the framework of GLMM is adopted. However, existing computational procedures for GLMM do not offer the flexibility to incorporate a desired correlation structure of defaults events. To overcome this limitation this thesis presents two feasible estimation techniques being the fixed effects (FE) approach and the two-step pseudo likelihood approach (2PL). The preciseness of the estimation techniques and their computational advantages are studied by Monte-Carlo simulations and by application to real data. The thesis also demonstrate the predictive likelihood approach to carry out predictive inference and the thesis supple- ments its demonstration with an R add-in package to facilitate the predictive inference for the GLMM. Örebro Studies in Statistics 5 örebro 2010 Doctoral Dissertation Feasible computation of the generalized linear mixed models with application to credit risk modelling Md. Moudud Alam Statistics Md. Moudud Alam Feasible computation of the generalized linear mixed models ...

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Page 1: Feasible computation of the generalized linear mixed ... › smash › get › diva2:360110 › COVER01.pdf · is the generalized linear models, and computational statistics and their

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ÖREBROSTUDIES INSTATISTICS

5

2010

issn 1651-8608 isbn 978-91-7668-771-0

Md. Moudud Alam (b. 1976) is a doctoral student in Statistics at Dalarna University. His main research interest is the generalized linear models, and computational statistics and their applications in economic and social research.

The thesis presents feasible computational procedures to estimate of and carry out prediction with the genera-lized linear mixed model (GLMM) for large data sets. This thesis is motivated from an issue of modelling the

probability of credit defaults with a large data set obtained from two major Swedish banks. To model the dependent credit defaults the framework of GLMM is adopted. However, existing computational procedures for GLMM do not offer the flexibility to incorporate a desired correlation structure of defaults events. To overcome this limitation this thesis presents two feasible estimation techniques being the fixed effects (FE) approach and the two-step pseudo likelihood approach (2PL). The preciseness of the estimation techniques and their computational advantages are studied by Monte-Carlo simulations and by application to real data. The thesis also demonstrate the predictive likelihood approach to carry out predictive inference and the thesis supple-ments its demonstration with an R add-in package to facilitate the predictive inference for the GLMM.

Örebro Studies in Statistics 5örebro 2010

Doctoral Dissertation

Feasible computation of the generalized linear mixed models with application to credit risk modelling

Md. Moudud AlamStatistics

Md

. Mo

ud

ud

Ala

m Feasible com

putation of the generalized linear mixed m

odels ...