A generalized Waring regression model for count data

Authors:
J. Rodríguez-Avi;A. Conde-Sánchez;A. J. Sáez-Castillo;M. J. Olmo-Jiménez;A. M. Martínez-Rodríguez
Affiliations:
Department of Statistics and Operations Research, University of Jaén, Spain;Department of Statistics and Operations Research, University of Jaén, Spain;Department of Statistics and Operations Research, University of Jaén, Spain;Department of Statistics and Operations Research, University of Jaén, Spain;Department of Statistics and Operations Research, University of Jaén, Spain
Venue:
Computational Statistics & Data Analysis
Year:
2009

Citing 5
Cited 1

Overdispersion: models and estimation

Computational Statistics & Data Analysis
A new generalization of the Waring distribution

Computational Statistics & Data Analysis
A framework for modelling overdispersed count data, including the Poisson-shifted generalized inverse Gaussian distribution

Computational Statistics & Data Analysis
Power series generalized nonlinear models

Computational Statistics & Data Analysis
Econometric Analysis of Count Data

Econometric Analysis of Count Data

A hyper-Poisson regression model for overdispersed and underdispersed count data

Computational Statistics & Data Analysis

Quantified Score

Hi-index	0.03

Visualization

Abstract

A regression model for count data based on the generalized Waring distribution is developed. This model allows the observed variability to be split into three components: randomness, internal differences between individuals and the presence of other external factors that have not been included as covariates in the model. An application in the field of sports illustrates its capacity for modelling data sets with great accuracy. Moreover, this yields more information than a model based on the negative binomial distribution.