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Approximate maximum product spacing estimation of half logistic distribution under progressive type II censored samples
Journal of the Korean Data & Information Science Society 2019;30:703-11
Published online May 31, 2019;
© 2019 Korean Data and Information Science Society.

Kyeongjun Lee1

1Dvision of Mathematics and Big Data Science, Daegu University
Correspondence to: Associate professor, Division of Mathematics and Big Data Science, Daegu University, Gyeongsan 38453, Korea. E-mail:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1C1B5018126).
Received April 6, 2019; Revised April 24, 2019; Accepted April 24, 2019.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
In most of the life testing and reliability experiments, the experimenter is often, unable to observe life time of all items put on test and the data avilable to the experimenter is censored data. Under classical estimation set up, the maximum product spacings method is quite effective and several authors advocated the use of this method as an alternative to MLE, and found that this estimation method provides better estimates than MLE in various situations. In this paper, we derive the MPSE for the parameter and reliability function of half-logistic (HL) distribution. And we derive the approximate MPSE for the parameter and reliability function of HL distribution using Talyor series expansion. We also compare the proposed estimators in the sense of the root mean squared error (RMSE) and bias for various progressive type II censored samples. In addition, real data example based on progressive type II censoring scheme have been also analysed for illustrative purposes.
Keywords : Approximate maximum product spacings estimation, half-logistic distribution, maximum product spacings estimation, progressive type II censoring.