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Statistical models for autocorrelated count data

2005; Wiley; Volume: 25; Issue: 8 Linguagem: Inglês

10.1002/sim.2274

1097-0258

Kerrie P. Nelson, Brian G. Leroux,

Statistical Distribution Estimation and Applications

Statistics in MedicineVolume 25, Issue 8 p. 1413-1430 Research Article Statistical models for autocorrelated count data Kerrie P. Nelson, Corresponding Author Kerrie P. Nelson [email protected] Department of Statistics, University of South Carolina, U.S.A.Department of Statistics, University of South Carolina, 1523 Greene Street, Columbia, SC 29208, U.S.A.Search for more papers by this authorBrian G. Leroux, Brian G. Leroux Department of Biostatistics, University of Washington, Seattle, U.S.A.Search for more papers by this author Kerrie P. Nelson, Corresponding Author Kerrie P. Nelson [email protected] Department of Statistics, University of South Carolina, U.S.A.Department of Statistics, University of South Carolina, 1523 Greene Street, Columbia, SC 29208, U.S.A.Search for more papers by this authorBrian G. Leroux, Brian G. Leroux Department of Biostatistics, University of Washington, Seattle, U.S.A.Search for more papers by this author First published: 30 September 2005 https://doi.org/10.1002/sim.2274Citations: 17AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat Abstract A generalized linear mixed model is an increasingly popular choice for the modelling of correlated, non-normal responses in a regression setting. A number of methods are currently available for fitting a generalized linear mixed model including Monte-Carlo Markov-Chain maximum likelihood algorithms, approximate maximum likelihood (PQL), iterative bias correction, and others. Of interest in this paper is to compare the parameter estimation of the various methods in the modelling of a count data set, the incidence of polio in the USA over the period 1970–1983, using a longlinear generalized linear mixed model with an autoregressive correlation structure. Despite the fact that all of these methods are considered valid modelling techniques, we find that parameter estimates and standard errors differ substantially between analyses, particularly in the estimation of the parameters describing the random effects distribution. A small simulation study is helpful in understanding some of these differences. The methods lead to reasonably similar predictions for future observations, with small differences observed in some monthly counts. 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Citing Literature Volume25, Issue830 April 2006Pages 1413-1430 ReferencesRelatedInformation