Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/42445
Title: Poisson-mixed inverse Gaussian regression model and its application
Authors: Gómez-Déniz, Emilio 
Ghitany, M. E.
Gupta, Ramesh C.
UNESCO Clasification: 12 Matemáticas
Keywords: Akaike information criterion
Maximum likelihood
Mixture inverse Gaussian distribution
Over-dispersion
Regression analysis estimation
Issue Date: 2016
Journal: Communications in Statistics Part B: Simulation and Computation 
Abstract: 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
Source: Communications in Statistics: Simulation and Computation[ISSN 0361-0918],v. 45, p. 2767-2781
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