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Likelihood prediction for generalized linear mixed models under covariate uncertainty
Örebro University, Swedish Business School at Örebro University.
2010 (English)Manuscript (preprint) (Other academic)
Abstract [en]

This paper presents the techniques of likelihood prediction for the generalized linear mixed models. Methods of likelihood prediction is explained through a series of examples; from a classical one to more complicated ones. The examples show, in simple cases, that the likelihood prediction (LP) coincides with already known best frequentist practice such as the best linear unbiased predictor. The paper outlines a way to deal with the covariate uncertainty while producing predictive inference. Using a Poisson error-in-variable general-ized linear model, it has been shown that in complicated cases LP produces better results than already know methods.

Place, publisher, year, edition, pages
2010.
Keyword [en]
Predictive likelihood, Pro…le predictive likelihood, Stochastic covariate, Coverage interval, Future value prediction, Credit risk prediction
National Category
Social Sciences Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:oru:diva-14079OAI: oai:DiVA.org:oru-14079DiVA: diva2:389407
Note

Mr Alam is also affiliated to Dalarna University, SE 781 88 Borlange, Sweden

Available from: 2011-01-19 Created: 2011-01-19 Last updated: 2012-12-14Bibliographically 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. 29 p.
Series
Örebro Studies in Statistics, ISSN 1651-8608 ; 5
Keyword
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: 2011-04-21Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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