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Dispersion models and longitudinal data analysis

✍ Scribed by Bent Jørgensen; Min Tsao

Publisher
John Wiley and Sons
Year
1999
Tongue
English
Weight
168 KB
Volume
18
Category
Article
ISSN
0277-6715

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✦ Synopsis

Dispersion models provide a #exible class of non-normal distributions with many potential applications in biostatistics, accommodating a wide range of continuous, discrete and mixed data. Starting with Liang and Zeger's generalized estimating equation method, we review some recent applications of dispersion models in longitudinal data analysis, including state space models based on the Tweedie class of exponential dispersion models. In medical applications the latent process of a state space model may often be interpreted as an unobserved potential morbidity process, which is modelled as a function of time varying covariates. By allowing a multivariate response vector of &symptoms', the model integrates several response variables of mixed types into a single model. For growth curve models, the latent process re#ects the &true' growth.

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