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Estimating the parameter of an exponential distribution under multiply type II hybrid censored sample
Journal of the Korean Data & Information Science Society 2024;35:275-84
Published online March 31, 2024;  https://doi.org/10.7465/jkdi.2024.35.2.275
© 2024 Korean Data and Information Science Society.

Kyeongjun Lee1

1Department of Mathematics and Big Data Science, Kumoh National Institute of Technology
Correspondence to: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2022R1I1A3068582).
1 Assistant professor, Department of Mathematics and Big Data Science, Kumoh National Institute of Technology, Gumi 39177, Korea. E-mail: indra_74@naver.com
Received February 28, 2024; Revised March 13, 2024; Accepted March 14, 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
There are also situations wherein the removal of units prior to failure is pre-planned in order to reduce the cost or time associated with testing. In this paper, we propose the estimators of the parameters of exponential distribution under multiply type II hybrid censoring scheme. Under classical estimation set up, the maximum product spacings (MPS) method is quite effective and several authors advocated the use of this method as an alternative to maximum likelihood (ML) method, and found that this estimation method provides better estimates than ML estimates in various situations. Therefore, we derive the ML estimator (MLE) and maximum product spacings estimator (MPSE) for the parameter of exponential distribution. Also, we derive the approximate MLEs and approximate MPSEs for the parameter of exponential distribution using Talyor series expansion. And we compare the proposed estimators in the sense of mean squared error (MSE) and bias under multiply type II hybrid censoring scheme. Finally, the validity of the proposed methods are demonstrated by a real data.
Keywords : Approximate maximum likelihood estimator, approximate maximum product spacings estimator, maximum likelihood estimator, maximum product spacings estimator, multiply type II hybrid censoring scheme