Approximate maximum product spacing estimation of Weibull distribution under multiply progressive censoring scheme†
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
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 27, 2023; Revised March 14, 2023; Accepted March 14, 2023.
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
- Under classical estimation set up, the maximum product spacings estimator (Max-PSE) method is quite effective and several authors advocated the use of this method as an alternative to maximum likelihood estimator (MLE), and found that this estimation method provides better estimates than maximum likelihood estimates in various situations. In this paper, we propose the estimators of the parameters of the Weibull distribution (WBD) under multiply progressive censoring (MultPC) scheme. First, we derive the MaxPSEs for the parameters of WBD under MultPC. Also, using Talyor series expansion, we derive the aMaxPSEs for the parameters of WBD under MultPC. A real data set based on the MultPC is analyzed for illustrative purposes.
- Keywords : Approximate maximum product spacings estimator, maximum product spacings estimator, multiply progressive censoring scheme, Taylor series expansion, Weibull distribuiton.
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