Hurdle negative binomial regression model with right censored count data
A Poisson model typically is assumed for count data. In many cases because of many zeros in the response variable, the mean is not equal to the variance value of the dependent variable. Therefore, the Poisson model is no longer suitable for this kind of data. Thus, we suggest using a hurdle negative...
| Autores: | , , |
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| Formato: | artículo |
| Fecha de publicación: | 2012 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2099/13324 |
| Acesso em linha: | https://hdl.handle.net/2099/13324 |
| Access Level: | acceso abierto |
| Palavra-chave: | Mathematical statistics Regression analysis hurdle negative binomial regression censored data maximum likelihood method simulation Estadística matemàtica Classificació AMS::62 Statistics::62J Linear inference, regression Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica |
| Resumo: | A Poisson model typically is assumed for count data. In many cases because of many zeros in the response variable, the mean is not equal to the variance value of the dependent variable. Therefore, the Poisson model is no longer suitable for this kind of data. Thus, we suggest using a hurdle negative binomial regression model to overcome the problem of overdispersion. Furthermore, the response variable in such cases is censored for some values. In this paper, a censored hurdle negative binomial regression model is introduced on count data with many zeros. The estimation of regression parameters using maximum likelihood is discussed and the goodness-of-fit for the regression model is examined |
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