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Estimating the parameters of exponentiated logistic distribution under progressive censoring scheme
Journal of the Korean Data & Information Science Society 2019;30:1441-52
Published online November 30, 2019;  https://doi.org/10.7465/jkdi.2019.30.6.1441
© 2019 Korean Data and Information Science Society.

Yeongjae Seong1 · Kyeongjun Lee2

12Dvision of Mathematics and Big Data Science, Daegu University
Correspondence to: Assistant professor, Division of Mathematics and Big Data Science, Daegu University, Gyeongsan 38453, Korea. E-mail: indra_74@naver.com

This work was supported by Daegu University Undergraduate Research Program, 2019.
Received October 21, 2019; Revised November 13, 2019; Accepted November 14, 2019.
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
The exponentiated logistic distribution can be considered as a proportional reversed hazard family with the baseline distribution as the logistic distribution. The exponentiated logistic distribution has been used to model the data with a unimodal density. The main aim of this paper is to propose the estimators of the parameters (when shape parameter is known) of the exponentiated logistic distribution under progressive censoring (PC) scheme. First, we derive the maximum product spacings estimators for parameters of exponentiated logistic distribution. And we derive the approximate maximum product spacings estimators for parameters of exponentiated logistic distribution using Talyor series expansions. We also compare the maximum product spacings estimators and approximate maximum product spacings estimators in the sense of the root mean squared error and bias for various PC schemes. In addition, real data example based on progressive censoring scheme have been also analysed for illustrative purposes.
Keywords : Approximate maximum product spacings estimation, exponentiated logistic distribution, maximum product spacings estimation, progressive censoring, Taylor series expansion.