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Exponentiated power generalized Weibull power series family of distributions: Properties, estimation and applications

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  • Maha A Aldahlan
  • Farrukh Jamal
  • Christophe Chesneau
  • Ibrahim Elbatal
  • Mohammed Elgarhy

Abstract

In this paper, we introduce the exponentiated power generalized Weibull power series (EPGWPS) family of distributions, obtained by compounding the exponentiated power generalized Weibull and power series distributions. By construction, the new family contains a myriad of new flexible lifetime distributions having strong physical interpretations (lifetime system, biological studies…). We discuss the characteristics and properties of the EPGWPS family, including its probability density and hazard rate functions, quantiles, moments, incomplete moments, skewness and kurtosis. The main vocation of the EPGWPS family remains to be applied in a statistical setting, and data analysis in particular. In this regard, we explore the estimation of the model parameters by the maximum likelihood method, with accuracy supported by a detailed simulation study. Then, we apply it to two practical data sets, showing the applicability and competitiveness of the EPGWPS models in comparison to some other well-reputed models.

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  • Maha A Aldahlan & Farrukh Jamal & Christophe Chesneau & Ibrahim Elbatal & Mohammed Elgarhy, 2020. "Exponentiated power generalized Weibull power series family of distributions: Properties, estimation and applications," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-25, March.
    • Handle: RePEc:plo:pone00:0230004
    DOI: 10.1371/journal.pone.0230004
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    References listed on IDEAS

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    Cited by:

    1. Abdulhakim A. Al-Babtain & Ibrahim Elbatal & Christophe Chesneau & Farrukh Jamal, 2020. "Box-Cox Gamma-G Family of Distributions: Theory and Applications," Mathematics, MDPI, vol. 8(10), pages 1-24, October.
    2. Rashad A. R. Bantan & Christophe Chesneau & Farrukh Jamal & Mohammed Elgarhy, 2020. "On the Analysis of New COVID-19 Cases in Pakistan Using an Exponentiated Version of the M Family of Distributions," Mathematics, MDPI, vol. 8(6), pages 1-20, June.
    3. Marlien Pieters & Iolanthe M. Kruger & Herculina S. Kruger & Yolandi Breet & Sarah J. Moss & Andries van Oort & Petra Bester & Cristian Ricci, 2023. "Strategies of Modelling Incident Outcomes Using Cox Regression to Estimate the Population Attributable Risk," IJERPH, MDPI, vol. 20(14), pages 1-9, July.
    4. Rana Muhammad Usman & Maryam Ilyas, 2024. "Power Burr X-T family of distributions: properties, estimation methods and real-life applications," Computational Statistics, Springer, vol. 39(6), pages 2949-2974, September.

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