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A new test statistic to assess the goodness of fit of exponential distribution under progressive censoring
Journal of the Korean Data & Information Science Society 2019;30:933-42
Published online July 31, 2019;  https://doi.org/10.7465/jkdi.2019.30.4.933
© 2019 Korean Data and Information Science Society.

Saemi Yun1 · Kyeongjun Lee2

12Division of Mathematics and Big Data Science, Daegu University
Correspondence to: Associate professor, Division of Mathematics and Big Data Science, Daegu University, Gyeongsan 38453, Korea. E-mail: indra_74@naver.comindra_74@naver.com

This work was supported by Daegu University Undergraduate Research Program, 2019.
Received June 20, 2019; Revised June 28, 2019; Accepted June 28, 2019.
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
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. In this paper, a new goodness-of-fit test for an exponential distribution based on progressive censored data is proposed. Using Monte Carlo simulation studies, the present researchers have observed that the proposed test for exponentiality is consistent and quite powerful in comparison with existing goodness-of-fit tests based on progressive censored data. Also, the new test statistic for a real data set is used and the results show that our new test statistic performs well.
Keywords : Exponential distribuiton, Lorenz curve, order statistics, progressive censoring.