New exponential dispersion models for count data: The ABM and LM classes

Shaul K. Bar-Lev, Ad Ridder

Research output: Contribution to journalArticlepeer-review

Abstract

In their fundamental paper on cubic variance functions (VFs), Letac and Mora (The Annals of Statistics, 1990) presented a systematic, rigorous and comprehensive study of natural exponential families (NEFs) on the real line, their characterization through their VFs and mean value parameterization. They presented a section that for some reason has been left unnoticed. This section deals with the construction of VFs associated with NEFs of counting distributions on the set of nonnegative integers and allows to find the corresponding generating measures. As EDMs are based on NEFs, we introduce in this paper two new classes of EDMs based on their results. For these classes, which are associated with simple VFs, we derive their mean value parameterization and their associated generating measures. We also prove that they have some desirable properties. Both classes are shown to be overdispersed and zero inflated in ascending order, making them as competitive statistical models for those in use in both, statistical and actuarial modeling. To our best knowledge, the classes of counting distributions we present in this paper, have not been introduced or discussed before in the literature. To show that our classes can serve as competitive statistical models for those in use (e.g., Poisson, Negative binomial), we include a numerical example of real data. In this example, we compare the performance of our classes with relevant competitive models.

Original languageEnglish
Pages (from-to)31-52
Number of pages22
JournalESAIM - Probability and Statistics
Volume25
DOIs
StatePublished - 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
© EDP Sciences, SMAI 2021.

Keywords

  • Exponential dispersion model
  • Natural exponential family
  • Overdispersion
  • Variance function
  • Zero-inflated distribution

ASJC Scopus subject areas

  • Statistics and Probability

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