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Journals / Hacettepe Journal of Mathematics and Statistics / 2020 / Cilt: 49 - Sayı: 6

Bayesian estimation of Rayleigh distribution in the presence of outliers using progressive censoring

Imran Baig
Farzana Noor
Ahthasham Sajid
Imranullah Khan
Mehwish Zaman
Maha Ghazal
Pages
2119–2133
Publication Date
2022-10-18
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Applicability of the undertaken study is highlighted using three real data sets.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this article, Maximum likelihood estimation (MLE) and Bayesian estimation for Rayleigh distribution using progressive type-II censoring in the presence of outliers is considered.
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Key Findings & Data:Inverse Gamma prior and Jeffreys prior are used for Bayesian estimation.
💡
Conclusion & Impact:Applicability of the undertaken study is highlighted using three real data sets.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Rayleigh distribution · outliers · progressive type-II censoring

Abstract

In this article, Maximum likelihood estimation (MLE) and Bayesian estimation for Rayleigh distribution using progressive type-II censoring in the presence of outliers is considered. Inverse Gamma prior and Jeffreys prior are used for Bayesian estimation. Squared error loss function (SELF), precautionary loss function (PLF) and K-loss function (KLF) are used for obtaining the expressions of Bayes estimators and posterior risks. Credible intervals are also derived. A simulation study is presented to discuss the behavior of Bayes estimators. Applicability of the undertaken study is highlighted using three real data sets.

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Cite This Article

Baig, I., Noor, F., Sajid, A., Khan, I., Zaman, M., Ghazal, M. (2022). Bayesian estimation of Rayleigh distribution in the presence of outliers using progressive censoring. Hacettepe Journal of Mathematics and Statistics, Cilt: 49 - Sayı: 6, 2119–2133. https://ajindex.com/articles/529615

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