oru.sePublikasjoner
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Likelihood prediction for generalized linear mixed models under covariate uncertainty
Örebro universitet, Handelshögskolan vid Örebro universitet.
2010 (engelsk)Manuskript (preprint) (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
2010.
Emneord [en]
Predictive likelihood, Pro…le predictive likelihood, Stochastic covariate, Coverage interval, Future value prediction, Credit risk prediction
HSV kategori
Forskningsprogram
Statistik
Identifikatorer
URN: urn:nbn:se:oru:diva-14079OAI: oai:DiVA.org:oru-14079DiVA, id: diva2:389407
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

Open Access i DiVA

fulltekst(209 kB)514 nedlastinger
Filinformasjon
Fil FULLTEXT01.pdfFilstørrelse 209 kBChecksum SHA-512
4f56e022aec674c96e306a76eccd53359bd04ba385914b5cac98fefa9f8fa145a546a43bc824c952ca0edea141e989f50188c3ae85e83fee468378212fdbf37e
Type fulltextMimetype application/pdf

Personposter BETA

Alam, Md. Moudud

Søk i DiVA

Av forfatter/redaktør
Alam, Md. Moudud
Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar
Totalt: 514 nedlastinger
Antall nedlastinger er summen av alle nedlastinger av alle fulltekster. Det kan for eksempel være tidligere versjoner som er ikke lenger tilgjengelige

urn-nbn

Altmetric

urn-nbn
Totalt: 230 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf