Consistency and asymptotic normality of the maximum likelihood estimator in a zero-inflated generalized Poisson regression - Claudia Czado; Aleksey Min (Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen, 2005)
Poisson regression models for count variables have been utilized in many applications. However, in many problems overdispersion and zeroinflation occur. We study in this paper regression models based on the generalized Poisson distribution (Consul (1989)). These regression models which have been used for about 15 years do not belong to the class of generalized linear models considered by McCullagh and Nelder (1989) for which an established asymptotic theory is available. Therefore we prove consistency and asymptotic normality of a solution to the maximum likelihood equations for zero-inflated generalized Poisson regression models. Further the accuracy of the asymptotic normality approximation is investigated through a simulation study. This allows to construct asymptotic confidence intervals and likelihood ratio tests.
urn:nbn:de:bvb:19-epub-1792-8
Keywords: 519; central limit theorem; likelihood; maximum likelihood estimator; overdispersion; zero-inflated generalized Poisson regression