Article Sidebar

Download
PDF
Statistic
Read Counter : 741 Download : 630

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Main Article Content

Abstract

In this work, a new distribution called the Chen Pareto distribution was derived using the Chen-G family of distributions. The mixture representation of the distribution was obtained. Furthermore, some statistical properties such as moments, moment generating functions, order statistics properties of the distribution were explored. The parameter estimation for the distribution was done using the maximum likelihood estimation method and the performance of estimators was assessed by conducting an extensive simulation study. The distribution was applied to a real dataset in which it performs best when compared to some related distributions

Keywords

Estimation Simulation Chen-G Pareto distribution Statistical properties

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following License

CC BY: This license allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.

 

How to Cite
Awodutire, P. (2020). Chen Pareto Distribution:Properties and Application. Pakistan Journal of Statistics and Operation Research, 16(4), 812-826. https://doi.org/10.18187/pjsor.v16i4.3418
Crossref
Scopus
Google Scholar
Europe PMC

References

  1. Anzagra,L. , Sarpong,S., Suleman Nasiru (2020).Chen-G class of distributions, Cogent Mathematics and Statistics, 7:1, 1721401
  2. Lee, S., Kim, J. H. (2018) Exponentiated generalized Pareto distribution: Properties and applications towards extreme value theory. Communications in Statistics-Theory and Methods.1-25.
  3. Brazauskas, V., Serfling, R.(2003). Favorable estimators for ï¬tting Pareto models: A study using goodness-of-ï¬t measures with actual data. ASTIN Bulletin: The Journal of the IAA. 33(2):365-81.
  4. Farshchian, M., Posner, F. L.(2010)The Pareto distribution for low grazing angle and high resolution X-band sea clutter. Naval Research Lab Washington DC. 5.
  5. Korkmaz, M., Altun, E., Yousof, H., Aï¬fy, A., Nadarajah, S. (2018). The Burr X Pareto Distribution: Properties, Applications and VaR Estimation. Journal of Risk and Financial Management.11(1):1
  6. Alzaatreh, A., Famoye, F., Lee, C. (2013) Weibull-Pareto distribution and its applications. Communica- tions in Statistics Theory and Methods.42(9):1673-1691.
  7. Akinsete A, Famoye F, Lee C. (2008) The beta-Pareto distribution. Statistics. 42(6):547-63.
  8. Pereira, M.B., Silva, R.B., Zea, L.M., Cordeiro, G.M. (2012) The kumaraswamy Pareto distribution. arXiv preprint arXiv:1204.1389.
  9. Gupta, R.C., Gupta, P.L., Gupta, R.D. (1998) Modeling failure time data by Lehman alternatives. Com- munications in Statistics-Theory and methods.27(4):887-904.
  10. Nadarajah, S. (2005) Exponentiated Pareto distributions. Statistics.39(3):255-60.
  11. Mahmoudi, E.(2011) The beta generalized Pareto distribution with application to lifetime data. Math- ematics and computers in Simulation.81(11):2414-2430.
  12. Alzaatreh, A., Famoye, F., Lee, C.(2012) Gamma Pareto Distribution and Applications, Journal of Modern Applied Statistical Methods. 11(1):7
  13. Faton, M., Llukan, P.(2014) Transmuted Pareto Distribution. ProbStat Forum. 7:1-11
  14. Shannon, C.E. (1948) A mathematical theory of communication. Bell Syst Tech J 27:379-423
  15. Akinsete, A., Famoye, Felix., Lee, Carl.(2008) The beta-Pareto distribution,Statistics,42:6,547- 563
  16. Rahman, M., Al-Zahrani, B., Shahbaz, M.Q. (2018) Cubic Transmuted Pareto Distribution, Annals of Data Science,https://doi.org/10.1007/s40745-018-0178-8
  17. Mudholkar, G.S., Huston, A.D.(1996) The exponentiated Weibull family: some properties and a flood data application. Commun Stat Theory Methods 23:1149-1171