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.
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