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The Odd Gamma Weibull-Geometric Model: Theory and Applications

Author

Listed:
  • Rana Muhammad Imran Arshad

    (Department of Statistics, The Islamia University of Bahawalpur, Punjab 63100, Pakistan)

  • Christophe Chesneau

    (Department of Mathematics, LMNO, University of Caen, 14032 Caen, France)

  • Farrukh Jamal

    (Department of Statistics, Govt. S.A Postgraduate College Dera Nawab Sahib, Bahawalpur, Punjab 63360, Pakistan)

Abstract

In this paper, we study a new four-parameter distribution called the odd gamma Weibull-geometric distribution. Having the qualities suggested by its name, the new distribution is a special member of the odd-gamma-G family of distributions, defined with the Weibull-geometric distribution as baseline, benefiting of their respective merits. Firstly, we present a comprehensive account of its mathematical properties, including shapes, asymptotes, quantile function, quantile density function, skewness, kurtosis, moments, moment generating function and stochastic ordering. Then, we focus our attention on the statistical inference of the corresponding model. The maximum likelihood estimation method is used to estimate the model parameters. The performance of this method is assessed by a Monte Carlo simulation study. An empirical illustration of the new distribution is presented by the analyses two real-life data sets. The results of the proposed model reveal to be better as compared to those of the useful beta-Weibull, gamma-Weibull and Weibull-geometric models.

Suggested Citation

  • Rana Muhammad Imran Arshad & Christophe Chesneau & Farrukh Jamal, 2019. "The Odd Gamma Weibull-Geometric Model: Theory and Applications," Mathematics, MDPI, vol. 7(5), pages 1-18, May.
  • Handle: RePEc:gam:jmathe:v:7:y:2019:i:5:p:399-:d:227951
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    References listed on IDEAS

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    1. Muhammad H Tahir & Gauss M. Cordeiro, 2016. "Compounding of distributions: a survey and new generalized classes," Journal of Statistical Distributions and Applications, Springer, vol. 3(1), pages 1-35, December.
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