Gamma regression models with the Gammareg R package
The class of gamma regression models is based on the assumption that the depen- dent variable is gamma distributed and that its mean is related to a set of regressors through a linear predictor with unknown coefficients and a link function. This link can be the identity, the inverse or the logarithm...
| Autor Principal: | Bossio, Martha Corrales; Facultad de Estadística Universidad Santo Tomás |
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| Otros Autores: | Cuervo, Edilberto Cepeda; Universidad Nacional de Colombia |
| Formato: | info:eu-repo/semantics/article |
| Idioma: | eng |
| Publicado: |
Universidad Santo Tomás
2015
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| Acceso en línea: |
http://revistas.usta.edu.co/index.php/estadistica/article/view/2050 |
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| Sumario: |
The class of gamma regression models is based on the assumption that the depen- dent variable is gamma distributed and that its mean is related to a set of regressors through a linear predictor with unknown coefficients and a link function. This link can be the identity, the inverse or the logarithm function. The model also includes a shape parameter, which may be constant or dependent on a set of regressors through a link function, as the logarithm function. In this paper we describe the Gammareg R-package, which provides the class of gamma regressions in the R system for their statistical computing. The underlying theory is briefly presented and the library implementation illustrated from simulation studies. |
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