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

Hazeb Rajaa (), Bayoud Husam A. () and Raqab Mohammad Z. ()
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Hazeb Rajaa: Department of Mathematics, The University of Jordan, Amman11942, Jordan
Bayoud Husam A.: College of Sciences and Humanities, Fahad Bin Sultan University, Tabuk, Saudi Arabia
Raqab Mohammad Z.: Department of Mathematics, The University of Jordan, Amman11942, Jordan

Stochastics and Quality Control, 2021, vol. 36, issue 1, 73-83

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.

Keywords: Entropy; Extropy; Uniformity; Mean Square Error; Nonparametric Statistics; Monte Carlo Simulation (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1515/eqc-2020-0035

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