10.7275/44NN-CJ68
Fávero, Luiz Paulo
Luiz Paulo
Fávero
Universidade de São Paulo
de Freitas Souza, Rafael
Rafael
de Freitas Souza
Universidade de São Paulo
Belfiore, Patrícia
Patrícia
Belfiore
Universidade Federal do ABC
Corrêa, Hamilton Luiz
Hamilton Luiz
Corrêa
Universidade de São Paulo
Haddad, Michael F. C.
Michael F. C.
Haddad
University of Cambridge
Count Data Regression Analysis: Concepts, Overdispersion Detection, Zero-inflation Identification, and Applications with R
University of Massachusetts Amherst
2021
In this paper is proposed a straightforward model selection approach that indicates the most suitable count regression model based on relevant data characteristics. The proposed selection approach includes four of the most popular count regression models (i.e. Poisson, negative binomial, and respective zero-inflated frameworks). Moreover, it addresses two of the most relevant problems commonly found in real-world count datasets, namely overdispersion and zero-inflation. The entire selection approach may be performed using the programme language R, being all commands used throughout the paper availabe for practical purposes. It is worth mentioning that counting regression models are still not widespread within the social sciences.