Transition Models for Count Data: a Flexible Alternative to Fixed Distribution Models
Methodology
2020-03-30 v1
Abstract
A flexible semiparametric class of models is introduced that offers an alternative to classical regression models for count data as the Poisson and negative binomial model, as well as to more general models accounting for excess zeros that are also based on fixed distributional assumptions. The model allows that the data itself determine the distribution of the response variable, but, in its basic form, uses a parametric term that specifies the effect of explanatory variables. In addition, an extended version is considered, in which the effects of covariates are specified nonparametrically. The proposed model and traditional models are compared by utilizing several real data applications.
Cite
@article{arxiv.2003.12411,
title = {Transition Models for Count Data: a Flexible Alternative to Fixed Distribution Models},
author = {Moritz Berger and Gerhard Tutz},
journal= {arXiv preprint arXiv:2003.12411},
year = {2020}
}
Comments
24 pages, 8 figures, 4 tables