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Estimation for exponential distribution under multiply progressive censored competing risks data†
Journal of the Korean Data & Information Science Society 2021;32:1305-15
Published online November 30, 2021;  https://doi.org/10.7465/jkdi.2021.32.6.1305
© 2021 Korean Data and Information Science Society.

Kyeongjun Lee1 · Hanse Kang2 · Soyun Jeong3 · Junki Hong4

1Division of Mathematics and Big Data Science, Daegu University
234Daegu Science High School
Correspondence to: 1 Corresponding author: Assistant professor, Division of Mathematics and Big Data Science, Daegu University, Gyeongbuk 38453, Korea. E-mail: indra 74@naver.com
2 Student, Daegu Science High School, Daegu 42110, Korea.
3 Student, Daegu Science High School, Daegu 42110, Korea.
4 Student, Daegu Science High School, Daegu 42110, Korea.
This research was supported by Daegu Science High School Research Program, 2021.
Received October 30, 2021; Revised November 15, 2021; Accepted November 17, 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
In lifetime data analysis, it is generally known that more than one cause or risk factor may be present at the same time. Also, generally, it is known that the lifetimes of test items may not be recorded exactly. Recently, progressive censoring schemes have become quite popular in a lifetime data analysis. But, there are many situation in life testing experiments in which units are lost or removed from experimentation before failure. In this reason, multiply progressive censoring scheme was introduced. Therefore, in this paper, we derive the maximum likelihood estimators and Bayes estimators of parameters for competing risks exponential data under multiply progressive censoring scheme. The Bayes estimators of parameters for the competing risks exponential distribution with multiply progressive censoring under the squared error loss function (SELF), precautionary loss function (PrL) and DeGroot loss function (DeL) are provided. Lindley’s approximate method is used to compute Bayes estimators. To know the performance of proposed estimators of parameters for competing risks exponentlai data under multiply progressive censoring scheme, a numerical study is conducted.
Keywords : Bayes estimation, competing risks model, exponential distribution, multiply progressive censoring.