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Product spacing estimation of half logistic distribution under progressive hybrid censoring
Journal of the Korean Data & Information Science Society 2022;33:337-46
Published online March 31, 2022;  https://doi.org/10.7465/jkdi.2022.33.2.337
© 2022 Korean Data and Information Science Society.

Kyeongjun Lee1

1Dvision of Mathematics and Big Data Science, Daegu University
Correspondence to: 1 Associate professor, Division of Mathematics and Big Data Science, Daegu University, Gyeongsan 38453, Korea. E-mail: indra_74@naver.com
This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2019S1A5A8034216).
Received February 25, 2022; Revised March 17, 2022; Accepted March 23, 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
In most of the life testing and reliability experiments, the experimenter is often, unable to observe life time of all items put on test and the data avilable to the experimenter is censored data. Among these censoring schemes, progressive censoring (PrC) is gaining rapid popularity. However, the drawback of the PrC is that it might take a very long time in order to complete the life test. In this reason, the progressive hybrid censoring scheme (PrHcs) was proposed. In this paper, we derive the maximum product spacings estimators (MaxPE) for the unknown parameter and reliability function (ReFtn) of half logistic distribution (HalfLD) under the PrHcs. And we derive the approximate maximum product spacings estimators (AppMaxPE) for the unknown parameter and ReFtn of HalfLD using Talyor series expansion. We also compare the estimators of unknown parameter and ReFtn in the terms of the mean squared error (MeanSqE) and bias for various PrHcs. In addition, real data example based on the PrHcs have been also analysed for illustrative purposes.
Keywords : Estimation, half logistic distribution, progressive hybrid censoring, spacing, Taylor series expansion.