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Estimation for half-logistic distribution based on generalized adaptive progressive hybrid censored sample
Journal of the Korean Data & Information Science Society 2021;32:405-16
Published online March 31, 2021;  https://doi.org/10.7465/jkdi.2021.32.2.405
© 2021 Korean Data and Information Science Society.

Subin Cho1 · Kyeongjun Lee2

1Department of Statistics, Daegu University
2Division of Mathematics and Big Data Science, Daegu University
Correspondence to: This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2019S1A5A8034216).
1Graduate student, Department of Statistics, Daegu University, Gyeongsan 38453, Korea.
2Corresponding author: Assistant professor, Division of Mathematics and Big Data Science, Daegu University, Gyeongsan 38453, Korea. E-mail: indra_74@naver.com
Received December 30, 2020; Revised February 18, 2021; Accepted March 12, 2021.
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
One of the disadvantages of the adaptive progressive hybrid censoring scheme is that the time of the experiment can be very long if units are highly reliable. Therefore, generalized adaptive progressive hybrid censoring scheme was proposed. In this article, the estimation of the parameter of half-logistic distribution based on the generalized adaptive progressive hybrid censored sample has been considered. The parameter is estimated by maximum likelihood estimator and approximate maximum likelihood estimator using Taylor series expansion. The Bayes estimator for the parameter of the half-logistic distribution based on the squared error loss function, are also provided. The Bayes estimators cannot be obtained explicitly, and Tierney and Kadane approximation is used to obtain the Bayes estimator. Simulation experiments are performed to see the effectiveness of the different estimators. Finally, a real dataset has been analyzed for illustrative purposes.
Keywords : Approximate maximum likelihood estimator, Bayes estimator, generalized adaptive progressive hybrid censoring scheme, half-logistic distribution, maximum likelihood estimator.