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

Q3 Mathematics Stochastics and Quality Control Pub Date : 2021-05-01 DOI:10.1515/eqc-2020-0035
Raja Hazeb, H. A. Bayoud, M. Z. Raqab
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Abstract

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.
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基于核和cdf的渐进式ii型滤波熵和熵估计及其拟合优度问题的应用
近年来,基于熵和外向性的均匀分布检验引起了一些研究者的关注。本文提出了基于渐进式ii型滤波的非参数熵和熵估计量,并研究了它们的性质和行为。通过仿真研究了所提估计器的性能。由性能良好的估计器开发了基于熵和外向性的均匀性拟合优度检验。通过模拟,对所提出的均匀性试验的功率进行了比较。
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来源期刊
Stochastics and Quality Control
Stochastics and Quality Control Mathematics-Discrete Mathematics and Combinatorics
CiteScore
1.10
自引率
0.00%
发文量
12
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