基于有序排序集合采样的雷利模型熵的贝叶斯推断

Q1 Decision Sciences

Annals of Data Science Pub Date : 2024-02-27 DOI:10.1007/s40745-024-00514-7

Mohammed S. Kotb, Haidy A. Newer, Marwa M. Mohie El-Din

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引用次数: 0

摘要

近来，有序集合样本方案在可靠性分析和寿命测试问题中颇受欢迎。基于有序排序集合样本，研究了雷利模型熵的贝叶斯估计值和可信区间，并与基于简单随机抽样的相应估计值进行了比较。这些贝叶斯熵估计器是用各种损失函数（如平方误差、线性-指数、Al-Bayyati 和一般熵损失函数）开发和计算的。对基于均方误差的各种熵估计值进行了比较研究。为了说明我们的程序，我们应用了真实数据集和模拟。

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Bayesian Inference for the Entropy of the Rayleigh Model Based on Ordered Ranked Set Sampling

Recently, ranked set samples schemes have become quite popular in reliability analysis and life-testing problems. Based on ordered ranked set sample, the Bayesian estimators and credible intervals for the entropy of the Rayleigh model are studied and compared with the corresponding estimators based on simple random sampling. These Bayes estimators for entropy are developed and computed with various loss functions, such as square error, linear-exponential, Al-Bayyati, and general entropy loss functions. A comparison study for various estimates of entropy based on mean squared error is done. A real-life data set and simulation are applied to illustrate our procedures.

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来源期刊

Annals of Data Science Decision Sciences-Statistics, Probability and Uncertainty

CiteScore

6.50

自引率

0.00%

发文量

期刊介绍： Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed. ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.