A generalized Waring regression model for count data. 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.
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References in zbMATH (referenced in 4 articles , 1 standard article )
Showing results 1 to 4 of 4.
- Rodríguez-Avi, José; Olmo-Jiménez, María José: A regression model for overdispersed data without too many zeros (2017)
- Gning, Lucien Diégane; Pierre-Loti-Viaud, Daniel: On the existence of maximum likelihood estimators in Poisson-gamma HGLM and negative binomial regression model (2013)
- Grunwald, Gary K.; Bruce, Stephanie L.; Jiang, Luohua; Strand, Matthew; Rabinovitch, Nathan: A statistical model for under- or overdispersed clustered and longitudinal count data (2011)
- Rodríguez-Avi, J.; Conde-Sánchez, A.; Sáez-Castillo, A.J.; Olmo-Jiménez, M.J.; Martínez-Rodríguez, A.M.: A generalized Waring regression model for count data (2009)