Linear Estimation in the Type II Generalized Logistic Distribution under Progressive Censoring

Abstract

Generalized distributions have become increasingly popular in applications. They are highly flexible in data analysis, especially with skewed data, which are common in many applications. The Generalized Logistic Distribution (GLD) and its special cases have recently received a lot of interest in the literature. We derived estimators of the unknown parameters of type II Generalized Logistic Distribution (Type II GLD) based on progressively type II censored data. A variety of point estimation methods is employed. We considered the best linear unbiased estimator (BLUE) and the best (affine) linear equivariant estimator (BLEE). In addition, we considered Bayesian estimation. Simulation approaches were used to study the estimators and compare them with the maximum likelihood estimator (MLE) in a range of progressive censoring schemes. The mean squared error (MSE) and bias were employed as comparison criteria. An example based on real data is presented.