A New Count Model Generated from Mixed Poisson Transmuted Exponential Family with an application to Health Care Data
| Autor: | Deepesh Bhati, E. Gómez–Déniz, Pooja Kumawat |
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| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
Statistics and Probability
FOS: Computer and information sciences Distribution (number theory) 05 social sciences Context (language use) Poisson distribution 01 natural sciences Unimodality Moment (mathematics) Methodology (stat.ME) 010104 statistics & probability symbols.namesake Exponential family Infinite divisibility (probability) 0502 economics and business symbols Applied mathematics 0101 mathematics Statistics - Methodology 050205 econometrics Mathematics Count data |
| Popis: | In this paper, 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 distribution as mixing distribution. Some distributional properties like unimodality, moments, over-dispersion, Taylor series expansion of proposed model are studied. Estimation of the parameters using method of moments, method of moments and proportion and maximum likelihood estimation along with data fitting experiment to show its advantage over some existing distribution. Further, an actuarial applications in context of aggregate claim distribution is discussed. Finally, we discuss a count regression model based on proposed distribution and its usefulness over some well established model. this files contains 24 pages excluding references, 2 figures |
| Databáze: | OpenAIRE |
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