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Likelihood prediction for generalized linear mixed models under covariate uncertainty
Dalarna University, School of Technology and Business Studies, Statistics.ORCID iD: 0000-0002-3183-3756
2014 (English)In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 43, no 2, 219-234 p.Article in journal (Refereed) Published
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 generalized linear model, it has been shown that in complicated cases LP produces better results than already know methods.

Place, publisher, year, edition, pages
Taylor & Francis Group, 2014. Vol. 43, no 2, 219-234 p.
Keyword [en]
Predictive likelihood, Profile predictive likelihood, Stochastic covariate, Coverage interval, Future value prediction, Credit risk prediction.
National Category
Probability Theory and Statistics
Research subject
Complex Systems – Microdata Analysis, Kreditriskmodellering; Complex Systems – Microdata Analysis, General Microdata Analysis - methods
URN: urn:nbn:se:du-13512DOI: 10.1080/03610926.2012.657330ISI: 000328930900001OAI: diva2:678826
Available from: 2013-12-13 Created: 2013-12-13 Last updated: 2015-12-11Bibliographically approved

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Alam, Moudud
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