Discrete Dispersion Models and Their Tweedie Asymptotics
Bent J{\o}rgensen, C\'elestin C. Kokonendji

TL;DR
This paper introduces a new class of discrete dispersion models called Poisson-Tweedie factorial dispersion models, unifying various overdispersed distributions and establishing a Poisson-Tweedie asymptotic framework with convergence results similar to classical limit theorems.
Contribution
It develops a novel class of discrete dispersion models using factorial tilting, extends Tweedie asymptotics to discrete settings, and explores their properties including dilation, duality, and multivariate extensions.
Findings
Poisson-Tweedie models unify overdispersed distributions.
Dilation leads to a Poisson-Tweedie asymptotic framework.
Duality transformation can switch between overdispersion and underdispersion.
Abstract
We introduce a class of two-parameter discrete dispersion models, obtained by combining convolution with a factorial tilting operation, similar to exponential dispersion models which combine convolution and exponential tilting. The equidispersed Poisson model has a special place in this approach, whereas several overdispersed discrete distributions, such as the Neyman Type A, P\'olya-Aeppli, negative binomial and Poisson-inverse Gaussian, turn out to be Poisson-Tweedie factorial dispersion models with power dispersion functions, analogous to ordinary Tweedie exponential dispersion models with power variance functions. Using the factorial cumulant generating function as tool, we introduce a dilation operation as a discrete analogue of scaling, generalizing binomial thinning. The Poisson-Tweedie factorial dispersion models are closed under dilation, which in turn leads to a…
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Taxonomy
TopicsStatistical Methods and Bayesian Inference · Bayesian Methods and Mixture Models · Hydrology and Drought Analysis
