Distributions to model overdispersed count data

Coly, Sylvain; Yao, Anne-Franoise; Abrial, David; Charras-Garrido, Myriam

Journal de la société française de statistique, Tome 157 (2016) no. 2, pp. 39-63.

Au début du vingtième siècle, seules quelques lois de comptage (loi binomiale, loi de Poisson) sont couramment utilisées en modélisation. Elles trouvent leur limite dans la modélisation de données bimodales ou surdispersées, notamment celles associées à un phénomène dont la survenue engendre d’autres occurrences. Pour les modéliser, de nouvelles lois sont élaborées, dites « lois contagieuses ». Ces distributions, telles que la loi binomiale négative, la loi de Neyman, la loi de Thomas ou encore la loi de Pólya-Aeppli, peuvent s’exprimer sous forme de mélange de lois usuelles ou encore de somme de variables aléatoires dont l’indice est borné par une variable aléatoire. Elles permettent d’ajuster des répartitions bimodales, surdispersées et très irrégulières. L’objectif de notre revue de la littérature est de décrire l’apparition de ces lois surdispersées, d’effectuer un descriptif de chacune de ces distributions, en s’intéressant en particulier à leurs différentes caractérisations, à leurs propriétés élémentaires et à leur utilisation potentielle, et enfin de les comparer en les appliquant à des données réelles surdispersées (cas de tuberculose bovine).

In the early twentieth century, only a few count distributions (binomial and Poisson distributions) were commonly used in modeling. These distributions fail to model bimodal or overdispersed data, especially data related to phenomena for which the occurrence of a given event increases the chance of additional events occurring. New count distributions have since been introduced to address such phenomena; they are named “contagious” distributions. This group of distributions, which includes the negative binomial, Neyman, Thomas and Pólya-Aeppli distributions, can be expressed as mixture distributions or as stopped-sum distributions. They take into account bimodality and overdispersion, and show a greater flexibility with regards to value distributions. The aim of this literature review is to 1) explain the introduction of these distributions, 2) describe each of these overdispersed distributions, focusing in particular on their definitions, their basic properties, and their practical utility, and 3) compare their strengths and weaknesses by modeling overdispersed real count data (bovine tuberculosis cases).

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