Open Accessmixture Poisson exponential–inverse Gaussian regression model
Author(s) -
Emilio Gómez–Déniz,
Enrique Calderín–Ojeda
Publication year - 2016
Publication title -
metodološki zvezki
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.127
H-Index - 7
eISSN - 1854-0031
pISSN - 1854-0023
DOI - 10.51936/rqpj6167
Subject(s) - inverse gaussian distribution , poisson regression , poisson distribution , statistics , exponential family , mathematics , exponential distribution , inverse , count data , regression analysis , zero inflated model , mixture model , natural exponential family , exponential function , dispersion (optics) , distribution (mathematics) , mathematical analysis , population , physics , medicine , geometry , environmental health , optics
In this paper a mixed Poisson regression model for count data is introduced. This model is derived by mixing the Poisson distribution with the one–parameter continuous exponential–inverse Gaussian distribution. The obtained probability mass function is over-dispersed and unimodal with modal value located at zero. Estimation is performed by maximum likelihood. As an application, the demand for health services among people 65 and over is examined using this regression model since empirical evidence has suggested that the over–dispersion and a large portion of non–users are common features of medical care utilization data.