Bayesian Computation Statistics
Iman Makhdoom; Shahram Yaghoobzadeh Shahrastani; FGhazalnaz Sharifonnasabi
Abstract
This study focuses on estimating the parameters of the Lindley distribution under a Type-II censoringscheme using Bayesian inference. Three estimation approaches—E-Bayesian, hierarchical Bayesian, andBayesian methods—are employed, with a focus on vague prior data. The accuracy of the estimates ...
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This study focuses on estimating the parameters of the Lindley distribution under a Type-II censoringscheme using Bayesian inference. Three estimation approaches—E-Bayesian, hierarchical Bayesian, andBayesian methods—are employed, with a focus on vague prior data. The accuracy of the estimates isevaluated using the entropy loss function and the squared error loss function (SELF). We assess theefficiency of the proposed methods through Monte Carlo simulations, utilizing the Lindley approximationand the Markov Chain Monte Carlo (MCMC) technique. To demonstrate its practical applicability, weapply the methodology to a real-world dataset to analyze the performance of the methods in detail.Comparative results from the simulations and data analysis reveal the robustness and accuracy of theproposed approaches. This comprehensive evaluation underscores the advantages of Bayesian methods inparameter estimation under censoring schemes, providing valuable insights for applications in reliabilityanalysis and related fields. The study concludes with a summary of key findings, offering a foundation forfurther exploration of Bayesian techniques in censored data analysis.
