Poisson-mixed Inverse Gaussian Regression Model and Its Application |
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| Authors: | Emilio Gómez-Déniz Ramesh C. Gupta |
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| Affiliation: | 1. Department of Quantitative Methods, University of Las Palmas de Gran Canaria, Gran Canaria, Spain;2. Department of Mathematics and Statistics, University of Maine, Orono, Maine, USA |
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| 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. |
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| Keywords: | Akaike information criterion Maximum likelihood Mixture inverse Gaussian distribution Over-dispersion Regression analysis estimation |
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