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Goodness of Fit Tests for the Log-Logistic Distribution Based on Cumulative Entropy under Progressive Type II Censoring

Department of Mathematics, Beijing Jiaotong University, Beijing 100044, China
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Mathematics 2019, 7(4), 361; https://doi.org/10.3390/math7040361
Received: 24 March 2019 / Revised: 11 April 2019 / Accepted: 16 April 2019 / Published: 20 April 2019
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Abstract

In this paper, we propose two new methods to perform goodness-of-fit tests on the log-logistic distribution under progressive Type II censoring based on the cumulative residual Kullback-Leibler information and cumulative Kullback-Leibler information. Maximum likelihood estimation and the EM algorithm are used for statistical inference of the unknown parameter. The Monte Carlo simulation is conducted to study the power analysis on the alternative distributions of the hazard function monotonically increasing and decreasing. Finally, we present illustrative examples to show the applicability of the proposed methods. View Full-Text
Keywords: log-logistic distribution; progressive Type II censoring; cumulative residual entropy; cumulative residual Kullback-Leibler information; expectation maximization algorithm; power analysis log-logistic distribution; progressive Type II censoring; cumulative residual entropy; cumulative residual Kullback-Leibler information; expectation maximization algorithm; power analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Du, Y.; Gui, W. Goodness of Fit Tests for the Log-Logistic Distribution Based on Cumulative Entropy under Progressive Type II Censoring. Mathematics 2019, 7, 361.

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