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Likelihood Prediction for Generalized Linear Mixed Models under Covariate Uncertainty
Sch Technol & Business Studies, Dalarna Univ, Falun, Sweden; Swedish Business Sch, Univ Örebro, Örebro, Sweden.
2014 (engelsk)Inngår i: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 43, nr 2, s. 219-234Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

This article presents the techniques of likelihood prediction for the generalized linear mixed models. Methods of likelihood prediction are 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. This article outlines a way to deal with the covariate uncertainty while producing predictive inference. Using a Poisson errors-in-variable generalized linear model, it has been shown in certain cases that LP produces better results than already known methods.

sted, utgiver, år, opplag, sider
2014. Vol. 43, nr 2, s. 219-234
Emneord [en]
Coverage interval, Credit risk prediction, Future value prediction, Predictive likelihood, Profile predictive likelihood, Stochastic covariate, Primary 62M20, Secondary 60G25, 62J12, 62J99
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Forskningsprogram
Matematik
Identifikatorer
URN: urn:nbn:se:oru:diva-33692DOI: 10.1080/03610926.2012.657330ISI: 000328930900001Scopus ID: 2-s2.0-84891593300OAI: oai:DiVA.org:oru-33692DiVA, id: diva2:695901
Tilgjengelig fra: 2014-02-12 Laget: 2014-02-12 Sist oppdatert: 2023-12-08bibliografisk kontrollert

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