A conditional likelihood approach to residual maximum likelihood estimation in generalized linear models

dc.contributor.authorSmyth, G.
dc.contributor.authorVerbyla, A.
dc.date.issued1996
dc.description.abstractResidual maximum likelihood (REML) estimation is often preferred to maximum likelihood estimation as a method of estimating covariance parameters in linear models because it takes account of the loss of degrees of freedom in estimating the mean and produces unbiased estimating equations for the variance parameters. In this paper it is shown that REML has an exact conditional likelihood interpretation, where the conditioning is on an appropriate sufficient statistic to remove dependence on the nuisance parameters. This interpretation clarifies the motivation for REML and generalizes directly to non-normal models in which there is a low dimensional sufficient statistic for the fitted values. The conditional likelihood is shown to be well defined and to satisfy the properties of a likelihood function, even though this is not generally true when conditioning on statistics which depend on parameters of interest. Using the conditional likelihood representation, the concept of REML is extended to generalized linear models with varying dispersion and canonical link. Explicit calculation of the conditional likelihood is given for the one-way lay-out. A saddlepoint approximation for the conditional likelihood is also derived.
dc.description.statementofresponsibilityGordon K. Smyth and Arunas P. Verbyla
dc.identifier.citationJournal- Royal Statistical Society Series B, 1996; 58(3):565-572
dc.identifier.issn0035-9246
dc.identifier.urihttp://hdl.handle.net/2440/5147
dc.language.isoen
dc.publisherWiley
dc.rights© 1996 Royal Statistical Society
dc.source.urihttp://www.jstor.org/stable/2345894
dc.subjectConditional Likelihood; Exponential Dispersion Model; Modified Profile Likelihood; One-Way Lay-Out; Residual Maximum Likelihood; Restricted Maximum Likelihood; Saddlepoint Approximation
dc.titleA conditional likelihood approach to residual maximum likelihood estimation in generalized linear models
dc.typeJournal article
pubs.publication-statusPublished

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