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Goodness-of-fit test for progressively censored data from gumbel distribution based on the decile dispersion ratio
Journal of the Korean Data & Information Science Society 2022;33:567-75
Published online July 31, 2022;  https://doi.org/10.7465/jkdi.2022.33.4.567
© 2022 Korean Data and Information Science Society.

Seonghee Park1 · Kyeongjun Lee2

1Department of Statistics, Daegu University
2Department of Big Data Science, Daegu University
Correspondence to: 1 Graduate student, Department of Statistics, Daegu University, Gyeongsan 38453, Korea.
2 Assistant professor, Department of Big Data Science, Daegu University, Gyeongsan 38453, Korea. E-mail: leekj@daegu.ac.kr
Received June 30, 2022; Revised July 13, 2022; Accepted July 19, 2022.
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
There are many areas of applications where Gumbel distribution are employed such as environmental sciences and hydrology. The goodness-of-fit test for Gumbel distribution is very important in environmental sciences and hydrology data analysis. In most of the life testing experiments, the experimenter is often, unable to observe life time of all items put on test and the data available to the experimenter is censored data. Therefore, we propose the test statistics to test goodness of fit for the Gumbel distribution under progressive type II censoring using decile dispersion ratio. We compare the new test statistic with the Yun and Lee (2018) test in terms of the power of the test through by Monte Carlo method. As a result, the new test statistics are more powerful than Yun and Lee (2018) test. Also, we check the proposed test statistics using ball baring data.
Keywords : Decile dispersion ratio, goodness-of-fit test, Gumbel distribution, progressive censoring.