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Comparison of Two New Robust Parameter Estimation Methods for the Power Function Distribution

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  • Muhammad Shakeel
  • Muhammad Ahsan ul Haq
  • Ijaz Hussain
  • Alaa Mohamd Abdulhamid
  • Muhammad Faisal

Abstract

Estimation of any probability distribution parameters is vital because imprecise and biased estimates can be misleading. In this study, we investigate a flexible power function distribution and introduced new two methods such as, probability weighted moments, and generalized probability weighted methods for its parameters. We compare their results with L-moments, trimmed L-moments by a simulation study and a real data example based on performance measures such as, mean square error and total deviation. We concluded that all the methods perform well in the case of large sample size (n>30), however, the generalized probability weighted moment method performs better for small sample size.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0160692
    DOI: 10.1371/journal.pone.0160692
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    Cited by:

    1. Safari, Muhammad Aslam Mohd & Masseran, Nurulkamal & Ibrahim, Kamarulzaman & AL-Dhurafi, Nasr Ahmed, 2020. "The power-law distribution for the income of poor households," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    2. 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.

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