MAXIMUM PRODUCT SPACING ESTIMATION FOR ODD LINDLEY HALF LOGISTIC DISTRIBUTION UNDER PROGRESSIVE TYPE-II CENSORING WITH BINOMIAL REMOVALS

Abstract

In this paper, we focus on parameter estimation for the odd Lindley half-logistic (OLiHL) distribution under a progressive type II censoring scheme based on binomial removals. The two estimation methods of maximum likelihood and maximum product spacing are employed for the one-parameter OLiHL distribution. To assess the performances of these two estimation methods under different progressive type II censoring schemes formed with binomial removals, a Monte Carlo simulation study is conducted. Moreover, to evaluate the estimation methods under progressive type II censoring, two real data applications are presented from the engineering and medical fields. Based on the results of the simulation study and real data applications, the maximum product spacing method performs better than the maximum likelihood method in estimating the parameter of the OLiHL distribution under progressive type II censoring.