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Estimation of the exponentiated half-logistic distribution under generalized type I hybrid censored samples
Journal of the Korean Data & Information Science Society 2021;32:1143-52
Published online September 30, 2021;  https://doi.org/10.7465/jkdi.2021.32.5.1143
© 2021 Korean Data and Information Science Society.

Kyeongjun Lee1

1Division of Mathematics and Big Data Science, Daegu University
Correspondence to: 1 Assistant professor, Division of Mathematics and Big Data Science, Daegu University, Gyeongsan 38453, Korea. E-mail: indra_74@naver.com
Received August 31, 2021; Revised September 17, 2021; Accepted September 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 this paper, we consider the shape parameter for the exponentiated half logistic distribution (ExHL) when samples are generalized type I hybrid censored samples. The shape parameter for the ExHL is estimated by the Bayesian method. We consider conjugate prior and corresponding posterior distribution is obtained. We also obtain the maximum likelihood estimator (MLE) of the shape parameter under the generalized type I hybrid censored samples (GenT1HCs). We compare the estimators in the sense of the root mean square error (RMSE). The simulation procedure is repeated 1,000 times for the sample size n = 20, 30, 40 and various generalized type I hybrid censored samples. Finally, a real data set has been analysed for illustrative purpose.
Keywords : Bayesian estimation, exponentiated half-logistic distribution, generalized type I hybrid censoring, Linex loss function, maximum likelihood estimation, squared error loss function.