Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/40344
Title: A new count model generated from mixed Poisson transmuted exponential family with an application to health care data
Authors: Bhati, Deepesh
Kumawat, Pooja
Gómez–Déniz, E. 
UNESCO Clasification: 1202 Análisis y análisis funcional
Keywords: Count regression
Health care data
Mixed Poisson distribution
Over-dispersion
Transmuted exponential family
Issue Date: 2017
Journal: Communications in Statistics - Theory and Methods 
Abstract: In this article, a new mixed Poisson distribution is introduced. This new distribution is obtained by utilizing mixing process, with Poisson distribution as mixed distribution and Transmuted Exponential as mixing distribution. Distributional properties like unimodality, moments, overdispersion, infinite divisibility are studied. Three methods viz. Method of moment, Method of moment and proportion, and Maximum-likelihood method are used for parameter estimation. Further, an actuarial application in context of aggregate claim distribution is presented. Finally, to showthe applicability and superiority of proposed model, we discuss count data and count regression modeling and compare with somewell established models.
URI: http://hdl.handle.net/10553/40344
ISSN: 0361-0926
DOI: 10.1080/03610926.2016.1257712
Source: Communications in Statistics - Theory and Methods[ISSN 0361-0926],v. 46, p. 11060-11076
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