1987
DOI: 10.2307/3314912
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Negative binomial and mixed Poisson regression

Abstract: A number of methods have been proposed for dealing with extra‐Poisson variation when doing regression analysis of count data. This paper studies negative‐binomial regression models and examines efficiency and robustness properties of inference procedures based on them. The methods are compared with quasilikelihood methods.

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Cited by 911 publications
(600 citation statements)
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References 29 publications
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“…For a fixed μ, NB gets closer to Poisson as ϑ → ∞ (For additional discussions and applications of Poisson mixture models, readers may refer to Grandell (1997), Hougaard, Lee, and Whitmore (1997) and J. Lawless (1987).) This paper is motivated by the problem of sample size and power calculations to design clinical trials in MS.…”
Section: Introductionmentioning
confidence: 99%
“…For a fixed μ, NB gets closer to Poisson as ϑ → ∞ (For additional discussions and applications of Poisson mixture models, readers may refer to Grandell (1997), Hougaard, Lee, and Whitmore (1997) and J. Lawless (1987).) This paper is motivated by the problem of sample size and power calculations to design clinical trials in MS.…”
Section: Introductionmentioning
confidence: 99%
“…Afin de vérifier si le modèle de Poisson est approprié on utilise la statistique S (partial score test, Lawless, 1987),…”
Section: Données De Comptage Pour Vétude Des Accidents Annuelsunclassified
“…Sa principale motivation est reliée aux externalités que certains conducteurs peuvent générer à d'autres individus (conducteurs, piétons) et qui ne peuvent être tarifées directement par différents marchés privés (Boyer et Dionne, 1984, 1985, 1987Dionne et al, 1995;Laberge-Nadeau et ai, 1996;Landes, 1983;Diamond, 1984). Les effets externes que génèrent certains conducteurs affectent les probabilités d'accidents des autres individus et leurs coûts.…”
Section: Introductionunclassified
“…If the data are overdispersed but this is ignored, we are overweighting the data and consequently underestimating their variability. The problem of overdispersion has been studied by many authors from several points of view (see for example, Lawless (1987); Dean and Lawless (1989); Hinde and Demetrio (1998);Thurston et al (2000)). It is natural to assume that the number of occurrence of events are realizations of a Poisson distribution and can be fitted by a Poisson regression model.…”
Section: Modelling Overdispersion With P -Splinesmentioning
confidence: 99%
“…Alternatively, Negative Binomial distribution has been used to allow independent modelling of the mean and variance by an additional parameter. Examples of modelling overdispersed data can be found in Lawless (1987); Dean and Lawless (1989); Hinde and Demetrio (1998); Thurston et al (2000).…”
Section: Introductionmentioning
confidence: 99%