A new count model based on Poisson-Transmuted Geometric convolution
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866910549419229184 |
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| author | Nandi, Anupama Chakraborty, Subrata Biswas, Aniket |
| author_facet | Nandi, Anupama Chakraborty, Subrata Biswas, Aniket |
| contents | A novel over-dispersed discrete distribution, namely the PoiTG distribution is derived by the convolution of a Poisson variate and an independently distributed transmuted geometric random variable. This distribution generalizes the geometric, transmuted geometric, and PoiG distributions. Various important statistical properties of this count model, such as the probability generating function, the moment generating function, the moments, the survival function, and the hazard rate function are investigated. Stochastic ordering for the proposed model are also studied in details. The maximum likelihood estimators of the parameters are obtained using general optimization approach and the EM algorithm approach. It is envisaged that the proposed distribution may prove to be useful for the practitioners for modelling over-dispersed count data compared to its closest competitors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_07219 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A new count model based on Poisson-Transmuted Geometric convolution Nandi, Anupama Chakraborty, Subrata Biswas, Aniket Statistics Theory 60E05, 62E15 A novel over-dispersed discrete distribution, namely the PoiTG distribution is derived by the convolution of a Poisson variate and an independently distributed transmuted geometric random variable. This distribution generalizes the geometric, transmuted geometric, and PoiG distributions. Various important statistical properties of this count model, such as the probability generating function, the moment generating function, the moments, the survival function, and the hazard rate function are investigated. Stochastic ordering for the proposed model are also studied in details. The maximum likelihood estimators of the parameters are obtained using general optimization approach and the EM algorithm approach. It is envisaged that the proposed distribution may prove to be useful for the practitioners for modelling over-dispersed count data compared to its closest competitors. |
| title | A new count model based on Poisson-Transmuted Geometric convolution |
| topic | Statistics Theory 60E05, 62E15 |
| url | https://arxiv.org/abs/2306.07219 |