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http://hdl.handle.net/10553/42445
| Título: | Poisson-mixed inverse Gaussian regression model and its application | Autores/as: | Gómez-Déniz, Emilio Ghitany, M. E. Gupta, Ramesh C. |
Clasificación UNESCO: | 12 Matemáticas | Palabras clave: | Akaike information criterion Maximum likelihood Mixture inverse Gaussian distribution Over-dispersion Regression analysis estimation |
Fecha de publicación: | 2016 | Publicación seriada: | Communications in Statistics Part B: Simulation and Computation | Resumen: | In this article, we have developed a Poisson-mixed inverse Gaussian (PMIG) distribution. The mixed inverse Gaussian distribution is a mixture of the inverse Gaussian distribution and its length-biased counterpart. A PMIG regression model is developed and the maximum likelihood estimation of the parameters is studied. A dataset dealing with the number of hospital stays among the elderly population is analyzed by using the PMIG and the PIG (Poisson-inverse Gaussian) regression models and it has been shown that the PMIG model fits the data better than the PIG model. | URI: | http://hdl.handle.net/10553/42445 | ISSN: | 0361-0918 | DOI: | 10.1080/03610918.2014.925924 | Fuente: | Communications in Statistics: Simulation and Computation[ISSN 0361-0918],v. 45, p. 2767-2781 |
| Colección: | Artículos |
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