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Lookup NU author(s): Emeritus Professor Murray Aitkin
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Random effect models have often been used in longitudinal data analysis since they allow for association among repeated measurements due to unobserved heterogeneity. Various approaches have been proposed to extend mixed models for repeated count data to include dependence on baseline counts. Dependence between baseline counts and individual-specific random effects result in a complex form of the (conditional) likelihood. An approximate solution can be achieved ignoring this dependence, but this approach could result in biased parameter estimates and in wrong inferences. We propose a computationally feasible approach to overcome this problem, leaving the random effect distribution unspecified. In this context, we show how the EM algorithm for nonparametric maximum likelihood (NPML) can be extended to deal with dependence of repeated measures on baseline counts. © Springer Science + Business Media, LLC 2006.
Author(s): Alfo M, Aitkin M
Publication type: Article
Publication status: Published
Journal: Statistics and Computing
Year: 2006
Volume: 16
Issue: 3
Pages: 231-238
ISSN (print): 0960-3174
ISSN (electronic): 1573-1375
Publisher: Springer Netherlands
URL: http://dx.doi.org/10.1007/s11222-006-7072-5
DOI: 10.1007/s11222-006-7072-5
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