Journal of Data Science and Modeling

Bayesian Inference for the Lindley Distribution under Type-II Censoring with Fuzzy Data

Document Type : Research Manuscript

Authors

Iman Makhdoom 1
Shahram Yaghoobzadeh Shahrastani 2
FGhazalnaz Sharifonnasabi 3

1 Payame Noor University

2 Department of Statistics, Payame Noor University, Tehran, Iran

3 School of Science and Technology, James Cook University, Singapore 387380, Singapore

https://doi.org/10.22054/jdsm.2025.83708.1061
Abstract
This study focuses on estimating the parameters of the Lindley distribution under a Type-II censoring
scheme using Bayesian inference. Three estimation approaches—E-Bayesian, hierarchical Bayesian, and
Bayesian methods—are employed, with a focus on vague prior data. The accuracy of the estimates is
evaluated using the entropy loss function and the squared error loss function (SELF). We assess the
efficiency of the proposed methods through Monte Carlo simulations, utilizing the Lindley approximation
and the Markov Chain Monte Carlo (MCMC) technique. To demonstrate its practical applicability, we
apply 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 the
proposed approaches. This comprehensive evaluation underscores the advantages of Bayesian methods in
parameter estimation under censoring schemes, providing valuable insights for applications in reliability
analysis and related fields. The study concludes with a summary of key findings, offering a foundation for
further exploration of Bayesian techniques in censored data analysis.

Keywords

E-Bayesian Estimation
hierarchical Bayesian estimation
Markov chain Monte Carlo (MCMC)
Lindley distribution
Type-II censoring scheme
vague data
Subjects

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