McDonald-G Poisson Family of Distributions
DOI:
https://doi.org/10.6092/issn.1973-2201/10520Keywords:
McDonald distribution, McDonald-G family, Truncated Poisson distribution, Parameter estimationAbstract
In this article, we utilize the method proposed by Tahir and Cordeiro (2016) to study a new family of distributions called the McDonald Generalized Poisson (McGP) family. This family is defined by using the genesis of the McDonald distribution and the zero truncated Poisson (ZTP) distribution. We provide some mathematical properties and parameter estimation procedures of the McGP family. Three real-life data are analyzed to illustrate the potential applications of the McGP family. Our examples illustrate that the development of new probability distributions is of great interest to capture the nature of the data under study. However, one can’t guarantee a better fit just because a probability distribution possesses a larger number of parameters than its sub-model.
References
Z. AHMAD, G. HAMEDANI, N. S. BUTT (2019). Recent developments in distribution theory: A brief survey and some newgeneralized classes of distributions. Pakistan Journal of Statistics and Operation Research, 15, no. 1, pp. 87–110.
D. K. AL-MUTAIRI, M. E.GHITANY, D. KUNDU (2013). Inferences on stress-strength reliability from Lindley distributions. Communications in Statistics - Theory and Methods, 42, no. 8, pp. 1443–1463.
G. R. ARYAL, S. B. CHHETRI, H. LONG, A. A. AKINSETE (2019). On the beta-G Poisson family. Annals of Data Science, 6, no. 3, pp. 361–389.
G. R. ARYAL, H. M. YOUSOF (2017). The exponentiated generalized-G poisson family of distributions. Stochastics and Quality Control, 32, no. 1, pp. 7–23.
R. C. CHENG, N. A. K. AMIN (1979). Product of spacings estimation with applications to the lognormal distribution. University ofWales IST, Math Report 79.
R. C. CHENG, N. A. K. AMIN (1983). Estimating parameters in continuous univariate distributions with a shifted origin. Journal of the Royal Statistical Society, Series B, 45, pp. 394–403.
F.N.DAVID,N. L. JOHNSON (1952). The truncated Poisson. Biometrics, 8, pp. 275–285.
M. E. GHITANY, B. ATIEH, S. NADARAJAH (2008). Lindley distribution and its application. Mathematics and Computers in Simulation, 78, pp. 493–506.
I. S. GRADSHTEYN, I. M. RYZHIK (2000). Table of Integrals, Series, and Products. 6th Edition, Academic Press, San Diego.
M. S. KHAN, R. KING, I. L. HUDSON (2019). Transmuted exponentiated Weibull distribution. Journal of Applied Probability and Statistics, 14, no. 2, pp. 37–51.
P. MARINHO, M. BOURGUIGNON, C. R. B. DIAS (2016). Adequacy of Probabilistic Models and Generation of Pseudo-Random Numbers, R package-AdequacyModel. URL http://cran.r-project.org/web/packages/AdequacyModel/AdequacyModel.pdf.
A. MARSHALL, I. OLKIN (2007). Life Distributions. Structure of Nonparametric, Semiparametric and Parametric Families. Springer-Verlag, New York.
J. B. MCDONALD (1984). Some generalized functions the size distribution of income. Econometrica, 52, no. 3, pp. 647–663.
D. MURTHY, M. XIE, R. JIANG (2004). Weibull Models. John Wiley & Sons, New York.
M. TAHIR, G. CORDEIRO (2016). Compounding of distributions: a survey and new generalized classes. Journal of Statistical Distributions and Applications, 3, no. 13, pp.1–35.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Statistica
This work is licensed under a Creative Commons Attribution 3.0 Unported License.