Estimation for half-logistic distribution based on generalized progressive hybrid censoring
Kyeongjun Lee1
1Department of Mathematics and Big Data Science, Kumoh National Institute of Technology
Correspondence to: 1 Assistant professor, Department of Mathematics and Big Data Science, Kumoh National Institute of Technology, Gumi 39177, Korea. E-mail: leekj@kumoh.ac.kr
Received August 27, 2024; Revised September 5, 2024; Accepted September 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
- One of the disadvantages of the progressive censoring scheme is that the time of the experiment can be very long if units are highly reliable. Therefore, generalized progressive hybrid censoring scheme was proposed. In this article, the estimation of the parameter of half-logistic distribution based on the generalized progressive hybrid censored sample has been considered. The parameter is estimated by maximum likelihood estimator, maximum product spacings estimator, approximate maximum likelihood estimator and approximate maximum product spacings estimator using Taylor series expansion. The Bayesian estimator for the parameter of the half-logistic distribution based on the symmetrical loss function, are also provided. The Bayesian estimator cannot be obtained explicitly, and Tierney and Kadane approximation is used to obtain the Bayesian 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 estimator, Bayesian estimator, generalized progressive hybrid censoring scheme, half-logistic distribution, maximum likelihood estimator, maximum product spacings estimator
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