Estimation of some lifetime parameter of the unit half logistic-geometry distribution under progressively type-II censored data

Abstract

This research examines the estimation of the parameter for the unit-half logistic-geometry distribution (UHLGD) using progressively type-II right censored samples. The unknown parameter of UHLGD under progressively type II right censoring is estimated using maximum likelihood estimations (MLEs), maximum product space estimators, and Bayesian estimators. The confidence intervals (ACIs) are computed. Additionally, two bootstrap CIs (bootstrap-p and bootstrap-t) are recommended. For symmetric loss functions such as squared error loss (SEL), Bayesian approximations are developed. The Metropolis-Hastings samplers approach is used to get Bayes estimates of unknown parameters and associated credible intervals (CRIs) using the Markov chain Monte Carlo (MCMC) method. A set of real data that measures the tensile strength of polyester fibres is considered an application of the provided techniques.