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
Appears in Collections:Artículos
Show full item record

SCOPUSTM   
Citations

3
checked on Apr 21, 2024

WEB OF SCIENCETM
Citations

2
checked on Feb 25, 2024

Page view(s)

55
checked on Apr 6, 2024

Google ScholarTM

Check

Altmetric


Share



Export metadata



Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.