Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/70163
Title: Quasi-binomial zero-inflated regression model suitable for variables with bounded support
Authors: Gómez Déniz, Emilio 
Gallardo, D. I.
Gómez, H. W.
UNESCO Clasification: 5302 Econometría
Keywords: Fit
Quasi Binomial Distribution
Score Test
Zero-Inflated
Cero, et al
Issue Date: 2020
Project: Nuevos Desarrollos en Métodos Cuantitativos Bayesianos. Aplicaciónes en Evaluación Económica de Tratamientos Mediante Meta-Análisis y Medición de Riesgos Con Datos Actuariales 
Aportaciones A la Toma de Decisiones Bayesianas Óptimas: Aplicaciones Al Coste-Efectividad Con Datos Clínicos y Al Análisis de Riestos Con Datos Acturiales. 
Journal: Journal of Applied Statistics 
Abstract: In recent years, a variety of regression models, including zero-inflated and hurdle versions, have been proposed to explain the case of a dependent variable with respect to exogenous covariates. Apart from the classical Poisson, negative binomial and generalised Poisson distributions, many proposals have appeared in the statistical literature, perhaps in response to the new possibilities offered by advanced software that now enables researchers to implement numerous special functions in a relatively simple way. However, we believe that a significant research gap remains, since very little attention has been paid to the quasi-binomial distribution, which was first proposed over fifty years ago. We believe this distribution might constitute a valid alternative to existing regression models, in situations in which the variable has bounded support. Therefore, in this paper we present a zero-inflated regression model based on the quasi-binomial distribution, taking into account the moments and maximum likelihood estimators, and perform a score test to compare the zero-inflated quasi-binomial distribution with the zero-inflated binomial distribution, and the zero-inflated model with the homogeneous model (the model in which covariates are not considered). This analysis is illustrated with two data sets that are well known in the statistical literature and which contain a large number of zeros.
URI: http://hdl.handle.net/10553/70163
ISSN: 0266-4763
DOI: 10.1080/02664763.2019.1707517
Source: Journal of Applied Statistics[ISSN 0266-4763], v. 47(12)
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