Identificador persistente para citar o vincular este elemento: 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
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