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Exponentiated Generalized Kumaraswamy Distribution with Applications

Author

Listed:
  • M. Elgarhy

    (Jeddah University
    Cairo University)

  • Muhammad Ahsan ul Haq

    (University of the Punjab)

  • Qurat Ain

    (The University of Lahore)

Abstract

In this article, we introduced and studied exponentiated generalized Kumaraswamy distribution. We derived mathematical properties including quantile function, moment generating function, ordinary moments, probability weighted moments, incomplete moments, and Rényi entropy. The expressions of order statistics are also derived. Here we discuss the parameter estimation by using the method of maximum likelihood. We showed resilience of the introduced distribution over existing some well-known distributions by using real dataset applications.

Suggested Citation

  • M. Elgarhy & Muhammad Ahsan ul Haq & Qurat Ain, 2018. "Exponentiated Generalized Kumaraswamy Distribution with Applications," Annals of Data Science, Springer, vol. 5(2), pages 273-292, June.
    • Handle: RePEc:spr:aodasc:v:5:y:2018:i:2:d:10.1007_s40745-017-0128-x
    DOI: 10.1007/s40745-017-0128-x
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    References listed on IDEAS

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    1. Ayman Alzaatreh & Carl Lee & Felix Famoye, 2013. "A new method for generating families of continuous distributions," METRON, Springer;Sapienza Università di Roma, vol. 71(1), pages 63-79, June.
    2. Muhammad Shakeel & Muhammad Ahsan ul Haq & Ijaz Hussain & Alaa Mohamd Abdulhamid & Muhammad Faisal, 2016. "Comparison of Two New Robust Parameter Estimation Methods for the Power Function Distribution," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-11, August.
    3. William T. Shaw & Ian R. C. Buckley, 2009. "The alchemy of probability distributions: beyond Gram-Charlier expansions, and a skew-kurtotic-normal distribution from a rank transmutation map," Papers 0901.0434, arXiv.org.
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    Cited by:

    1. Farha Sultana & Yogesh Mani Tripathi & Shuo-Jye Wu & Tanmay Sen, 2022. "Inference for Kumaraswamy Distribution Based on Type I Progressive Hybrid Censoring," Annals of Data Science, Springer, vol. 9(6), pages 1283-1307, December.

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