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Feasible estimation of generalized linear mixed models (GLMM) with weak dependency between groups
Örebro universitet, Handelshögskolan vid Örebro universitet.
2010 (engelsk)Manuskript (preprint) (Annet vitenskapelig)
Abstract [en]

This paper presents a two-step pseudo likelihood estimation for generalized linear mixed models with the random effects being correlated between groups. The core idea is to deal with the random intractable integrals in  the likelihood function by multivariate Taylor's approximation. The accuracy of the estimation technique is assessed in a Monte-Carlo study: An application of it with binary response variable is presented using a real dara set on credit defaults from two Swedish banks. Thanks to   the use of two-step estimation technique, the proposed algorithm outperforms conventional likelihood algoritms in terms of computational time.

sted, utgiver, år, opplag, sider
2010.
Emneord [en]
PQL, Laplace approximation, interdependence, cluster errrors, credit risk model
HSV kategori
Forskningsprogram
Statistik
Identifikatorer
URN: urn:nbn:se:oru:diva-14061OAI: oai:DiVA.org:oru-14061DiVA, id: diva2:389313
Merknad

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

Tilgjengelig fra: 2011-01-19 Laget: 2011-01-19 Sist oppdatert: 2017-10-17bibliografisk kontrollert
Inngår i avhandling
1. Feasible computation of generalized linear mixed models with application to credit risk modelling
Åpne denne publikasjonen i ny fane eller vindu >>Feasible computation of generalized linear mixed models with application to credit risk modelling
2010 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Örebro: Örebro universitet, 2010. s. 29
Serie
Örebro Studies in Statistics, ISSN 1651-8608 ; 5
Emneord
Credit risk, cluster correlation, GLMM, large data, two-step pseudo likelihood estimation, defaults contagion, predictive likelihood
HSV kategori
Forskningsprogram
Statistik
Identifikatorer
urn:nbn:se:oru:diva-12390 (URN)978-91-7668-771-0 (ISBN)
Disputas
2010-12-21, Hörsal M, Örebro universitet, Fakultetsgatan 1, 701 82 Örebro, 15:15 (engelsk)
Opponent
Tilgjengelig fra: 2010-11-04 Laget: 2010-11-02 Sist oppdatert: 2017-10-17bibliografisk kontrollert

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