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Bayesian Inference for the Lindley Distribution under Type-II Censoring with Fuzzy Data | ||
| Journal of Data Science and Modeling | ||
| دوره 2، شماره 2 - شماره پیاپی 4، شهریور 2024، صفحه 245-265 اصل مقاله (318.03 K) | ||
| نوع مقاله: Research Manuscript | ||
| شناسه دیجیتال (DOI): 10.22054/jdsm.2025.83708.1061 | ||
| نویسندگان | ||
| Iman Makhdoom* 1؛ Shahram Yaghoobzadeh Shahrastani2؛ FGhazalnaz Sharifonnasabi3 | ||
| 1Payame Noor University | ||
| 2Department of Statistics, Payame Noor University, Tehran, Iran | ||
| 3School of Science and Technology, James Cook University, Singapore 387380, Singapore | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| E-Bayesian Estimation؛ hierarchical Bayesian estimation؛ Markov chain Monte Carlo (MCMC)؛ Lindley distribution؛ Type-II censoring scheme؛ vague data | ||
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آمار تعداد مشاهده مقاله: 430 تعداد دریافت فایل اصل مقاله: 263 |
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