Linear approximate ml estimation of scale parameter in type II generalized half-logistic distribution under type-II censoring

Abstract

The two-parameter Type II generalized half-logistic distribution (GHLD) with known shape parameter is considered here and the ML method does not yield explicit estimator for scale parameter even in complete samples. Therefore, in this paper, we have constructed two linear estimators for scale parameter; one is obtained by making linear approximations to the intractable terms of the maximum likelihood equation of the scale parameter and another is obtained using percentile estimation method. We call these two estimators as linear approximate MLE (LAMLE) and percentile estimator (PCE) respectively. To investigate the performance of LAMLE and PCE, a Monte Carlo simulation is made to compare them with the MLE. Further, we have constructed nearly unbiased LAMLE and nearly unbiased PCE and compared them with MLE through a simulation study. Finally, a numerical example is presented to illustrate the construction of the new estimators developed here.