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Approximate maximum product spacings estimation of exponential distribution under multiply hybrid censored data
Journal of the Korean Data & Information Science Society 2023;34:167-75
Published online January 31, 2023;
© 2023 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:
Received December 30, 2022; Revised January 12, 2023; Accepted January 14, 2023.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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. In this paper, we propose the estimators of the parameter of the exponential distribution (ExDst) under multiply hybrid censoring scheme (MHCS). First, we derive the ML estimator (MLE) and MPS estimator (MPSE) for the parameter of ExDst. Also, we derive the approximate MPSEs for the parameter of ExDst using Talyor series expansion. And we compare the proposed estimators in the sense of mean squared error (MSE) and bias under MHCS. Finally, the validity of the proposed methods are demonstrated by a real data.
Keywords : Approximate maximum product spacings estimator, maximum likelihood estimator, maximum product spacings estimator, multiply hybrid censoring scheme, exponential distribuiton.