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Two ways of modelling overdispersion in non‐normal data

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  • Y. Lee
  • J. A. Nelder

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.

Suggested Citation

  • Y. Lee & J. A. Nelder, 2000. "Two ways of modelling overdispersion in non‐normal data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(4), pages 591-598.
    • Handle: RePEc:bla:jorssc:v:49:y:2000:i:4:p:591-598
    DOI: 10.1111/1467-9876.00214
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    Cited by:

    1. Aghabazaz, Zeynab & Kazemi, Iraj, 2023. "Under-reported time-varying MINAR(1) process for modeling multivariate count series," Computational Statistics & Data Analysis, Elsevier, vol. 188(C).

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