Bayesian Estimation of a Geometric Life Testing Model under Different Loss Functions Using a Doubly Type-1Censoring Scheme

Abstract

In this article, we consider the doubly type-1 censoring scheme that researchers frequently use in clinical trials and lifetime experiments. The Bayesian paradigm will be used to estimate the parameters of the Geometric Lifetime Model (GLTM) using a doubly type-I censoring scheme. Bayes estimators and their associated Bayes risks are examined in terms of closed-form algebraic expressions. This research also includes a strategy for eliciting hyperparameters based on prior prediction distributions. To evaluate the strength and effectiveness of the suggested estimating approach, thorough simulation studies as well as real-life data analysis are presented. The results depict that Squared Error Loss Function (SELF) is more efficient, and the Beta prior is suitable while estimating the parameter of GLTM.