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Goodness-of-fit test for progressive censored data from an inverted exponentiated half logistic distribution
Journal of the Korean Data & Information Science Society 2024;35:111-21
Published online January 31, 2024;  https://doi.org/10.7465/jkdi.2024.35.1.111
© 2024 Korean Data and Information Science Society.

Kyeongjun Lee 1

1Department of Mathematics and Big Data Science, Kumoh National Institute of Technology
Correspondence to: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2022R1I1A3068582).
1 Assistant professor, Department of Mathematics and Big Data Science, Kumoh National Institute of Technology, Gyeongbuk 39177, Korea. E-mail: indra_74@naver.com
Received December 20, 2023; Revised January 5, 2024; Accepted January 5, 2024.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Type I and Type II censoring schemes are the most common censoring schemes. However, these censoring schemes do not allow for items to be withdrew from the experiment at times other than the termination time. These motivation lead reliability practitioners directly into the area of progressive censoring. Also, the problem of examining how well a assumed distribution fits the data of a sample is of significant that has to be examined prior to any inferential process. Therefor, we derives an maximum likelihood estimator of the parameters of inverted half logistic distribution. And we introduced the goodness-of-fit test statistics (using order statistics, decile dispersion ratio and Lorenz curve) and carried out Monte Carlo simulation to compare the proposed test statistics. In addition, real data set have been analyzed.
Keywords : Decile dispersion ratio, goodness of fit test, inverted exponentiated half logistic distribution, Lorenz curve, order statistic, progressive censoring.