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

Yuge Du and Wenhao Gui
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Yuge Du: Department of Mathematics, Beijing Jiaotong University, Beijing 100044, China
Wenhao Gui: Department of Mathematics, Beijing Jiaotong University, Beijing 100044, China

Mathematics, 2019, vol. 7, issue 4, 1-20

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.

Keywords: log-logistic distribution; progressive Type II censoring; cumulative residual entropy; cumulative residual Kullback-Leibler information; expectation maximization algorithm; power analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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