GWRM

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 9 articles , 1 standard article )

Showing results 1 to 9 of 9.
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  1. Altun, Emrah: A new model for over-dispersed count data: Poisson quasi-Lindley regression model (2019)
  2. Rodríguez-Avi, José; Olmo-Jiménez, María José: A regression model for overdispersed data without too many zeros (2017)
  3. Wongrin, Weerinrada; Bodhisuwan, Winai: Generalized Poisson-Lindley linear model for count data (2017)
  4. Chan, Jennifer So Kuen; Wan, Wai Yin: Bayesian analysis of Cannabis offences using generalized Poisson geometric process model with flexible dispersion (2016)
  5. Gning, Lucien Diégane; Pierre-Loti-Viaud, Daniel: On the existence of maximum likelihood estimators in Poisson-gamma HGLM and negative binomial regression model (2013)
  6. Sáez-Castillo, A. J.; Conde-Sánchez, A.: A hyper-Poisson regression model for overdispersed and underdispersed count data (2013)
  7. Faddy, M. J.; Smith, D. M.: Analysis of count data with covariate dependence in both mean and variance (2011)
  8. 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)
  9. 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)