A new count model generated from mixed Poisson transmuted exponential family with an application to health care data

Deepesh Bhati, Pooja Kumawat and E. Gómez–Déniz

Communications in Statistics - Theory and Methods, 2017, vol. 46, issue 22, 11060-11076

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, over-dispersion, 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 show the applicability and superiority of proposed model, we discuss count data and count regression modeling and compare with some well established models.

Date: 2017
References: Add references at CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2016.1257712 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:46:y:2017:i:22:p:11060-11076

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2016.1257712

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst (chris.longhurst@tandf.co.uk).