Derivation of Two Parameters Poisson Rani Distribution and Its Properties
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Abstract
This study introduces the Two Parameters Poisson Rani Distribution (TPPRD). The probability distribution of TPPRD is derived by assuming that the parameters of the Poisson distribution follow the Two Parameters Rani Distribution, resulting in the formation of the TPPRD. The study derives some of its fundamental properties and demonstrates that TPPRD is a special-case distribution capable of handling overdispersed count data. Additionally, the maximum likelihood estimators are used to derive equations for estimating the parameters of the Two Parameters Poisson Rani Distribution.
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References
Ahmad A. H. & Amjad D. A. (2021). New Distribution for Fitting Discrete Data: The Poisson-Gold Distribution and Its Statistical Properties. Austrian Journal of Statistics. Vol. 50, pp. 19-35.
Emrah A. (2018). A new zero-inflated regression model with application. Journal of Statisticians: Statistics and Actuarial Sciences. 2, 73-80. www.istatistikciler.org.
Lawal, B. H. (2011). On the Negative Binomial Generalized Exponential Distribution and its Applications. Applied Mathematical Sciences, Vol. 11, No. 8, pp. 345-360. https://doi.org/10.12988/ams.2017.612288.
Maya, R., Irshad, M.R., Chesneau, C., Nitin, S.L. & Shibu, D.S. (2022). On Discrete Poisson–Mirra Distribution: Regression, INAR(1) Process and Applications. Axioms, 11, 193, pp. 1-27. https://doi.org/ 10.3390/axioms11050193.
Mohamed, S. E., Muhammad, A., Amani, A., Afrah, A., & Mahmoud E. (2023). Discrete Extension of Poisson Distribution for Overdispersed Count Data: Theory and Applications. Journal of Mathematics, Vol. 2023, pp. 1-15. https://doi.org/10.1155/2023/2779120.
Noemi, C. & Cinzia V. (2022). Dealing with overdispersion in multivariate count data. Computational Statistics & Data Analysis. Vol. 170, Page: 107447. https://doi.org/10.1016/j.csda.2022.107447.
Showkat A. D., Anwar, H. & Peer, B. A., & Mansour, L. (2022). On Poisson Weighted Pranav Distribution Applicable to Count Data. An International Journal of Statistics Applications & Probability. 11(2). Pp. 385-393. http://dx.doi.org/10.18576/jsap/110202.
Yupapin A. (2023). A New Poisson Generalized Lindley Regression Model. Austrian Journal of Statistics. Volume 52, 39-50. http://www.ajs.or.at/doi:10.17713/ajs.v52i1.1344.
Zamani, H, Faroughi, P. & Ismail, N. (2015). Bivariate Poisson-Lindley Distribution with Application. Journal of Mathematics and Statistics. 11(1): pp.1-6. doi: 10.3844/jmssp.2015.1.6
Zamani, H., Ismail, N. & Shekari, M. (2018). Weighted Negative Binomial-Poisson Lindley with Application to Genetic Data. J Biostat Epidemiol. 4(3): pp. 136-141.




















