Abstract
In this paper, Robust Bayesian analysis of the generalized half logistic distribution (GHLD) under an $\epsilon$-contamination class of priors for the shape parameter $\lambda$ is considered. ML-II Bayes estimators of the parameters, reliability function and hazard function are derived under the squared-error loss function (SELF) and linear exponential (LINEX) loss function by considering the Type~II censoring and the sampling scheme of Bartholomew (1963). Both the cases when scale parameter is known and unknown is considered under Type~II censoring and under the sampling scheme of Bartholomew. Simulation study and analysis of a real data set are presented.
Highlights
Introduction and preliminariesHalf logistic has been used in many reliability and survival analysis
Bhimani and Patel (2010) obtained the maximum likelihood estimator of the shape parameter in a generalized half logistic distribution (GHLD) based on Type I progressive censoring with varying failure rates
Kang and Seo (2011) proposed the Bayes estimators of the shape parameter and reliability function for the GHLD based on progressively Type II censored data under various loss functions
Summary
Half logistic has been used in many reliability and survival analysis (especially when the data is censored). Seo and Kang (2014) derived the entropy of GHLD by using the Bayes estimators of an unknown parameter based on Type II censored samples. Kim, Kang and Seo (2011) proposed the Bayes estimators of the shape parameter and reliability function for the GHLD based on progressively Type II censored data under various loss functions.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
