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Kernel and CDF-Based Estimation of Extropy and Entropy from Progressively Type-II Censoring with Application for Goodness of Fit Problems

Author

Listed:
  • Hazeb Rajaa
  • Raqab Mohammad Z.

    (Department of Mathematics, The University of Jordan, Amman11942, Jordan)

  • Bayoud Husam A.

    (College of Sciences and Humanities, Fahad Bin Sultan University, Tabuk, Saudi Arabia)

Abstract

Recently, entropy and extropy-based tests for the uniform distribution have attracted the attention of some researchers. This paper proposes nonparametric entropy and extropy estimators based on progressive type-II censoring and investigates their properties and behavior. Performance of the proposed estimators is studied via simulations. Entropy and extropy-based goodness-of-fit tests for uniformity are developed by the well performed estimators. The powers of the proposed uniformity tests are compared also via simulations assuming various alternatives and censoring schemes.

Suggested Citation

  • Hazeb Rajaa & Raqab Mohammad Z. & Bayoud Husam A., 2021. "Kernel and CDF-Based Estimation of Extropy and Entropy from Progressively Type-II Censoring with Application for Goodness of Fit Problems," Stochastics and Quality Control, De Gruyter, vol. 36(1), pages 73-83, June.
    • Handle: RePEc:bpj:ecqcon:v:36:y:2021:i:1:p:73-83:n:2
    DOI: 10.1515/eqc-2020-0035
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