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Estimation for the half-logistic distribution based on generalized progressive hybrid censoring
Journal of the Korean Data & Information Science Society 2018;29:1049-59
Published online July 31, 2018
© 2018 Korean Data and Information Science Society.

Sung-Ok Lee1 · Suk-Bok Kang2

12Department of Statistics, Yeungnam University
Correspondence to: Professor, Department of Statistics, Yeungnam University, Gyeongsan 38541,
Korea. E-mail: sbkang@yu.ac.kr
Received May 8, 2018; Revised June 8, 2018; Accepted June 11, 2018.
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
We obtain maximum likelihood estimator (MLE) and approximate maximum like-lihood estimators (AMLEs) of the scale parameter in half-logistic distribution under generalized progressive hybrid censored samples. We also obtain a Bayes estimator of the scale parameter under squared error loss function. Finally, we examine the validity of the proposed estimators using simulated data and real data. AMLEs are obtained explicitly with closed form and AMLEs are more ecient than MLE. Bayes estimator of the scale parameter is more ecient than MLE and AMLEs.
Keywords : Approximate maximum likelihood estimators, generalized progressive hybrid censored sample, half-logistic distribution, maximum likelihood estimator.