Name
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1080/03610918.2014.925924 | COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION |
Keywords | Field | DocType |
Akaike information criterion,Maximum likelihood,Mixture inverse Gaussian distribution,Over-dispersion,Regression analysis estimation | Econometrics,Population,Overdispersion,Akaike information criterion,Inverse Gaussian distribution,Regression analysis,Generalized inverse Gaussian distribution,Gaussian,Poisson distribution,Statistics,Mathematics | Journal |
Volume | Issue | ISSN |
45 | 8 | 0361-0918 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Emilio Gómez-Déniz | 1 | 4 | 3.59 |
| M.E. Ghitany | 2 | 75 | 14.09 |
| Ramesh C. Gupta | 3 | 44 | 12.98 |