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Two ways of modelling overdispersion in non‐normal data

Two ways of modelling overdispersion in non‐normal data For non‐normal data assumed to have distributions, such as the Poisson distribution, which have an a priori dispersion parameter, there are two ways of modelling overdispersion: by a quasi‐likelihood approach or with a random‐effect model. The two approaches yield different variance functions for the response, which may be distinguishable if adequate data are available. The epilepsy data of Thall and Vail and the fabric data of Bissell are used to exemplify the ideas. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of the Royal Statistical Society: Series C (Applied Statistics) Wiley

Two ways of modelling overdispersion in non‐normal data

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References (16)

Publisher
Wiley
Copyright
Copyright © 2000 Wiley Subscription Services, Inc., A Wiley Company
ISSN
0035-9254
eISSN
1467-9876
DOI
10.1111/1467-9876.00214
Publisher site
See Article on Publisher Site

Abstract

For non‐normal data assumed to have distributions, such as the Poisson distribution, which have an a priori dispersion parameter, there are two ways of modelling overdispersion: by a quasi‐likelihood approach or with a random‐effect model. The two approaches yield different variance functions for the response, which may be distinguishable if adequate data are available. The epilepsy data of Thall and Vail and the fabric data of Bissell are used to exemplify the ideas.

Journal

Journal of the Royal Statistical Society: Series C (Applied Statistics)Wiley

Published: Jan 1, 2000

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