双 1 型普查方案下多成分几何寿命测试模型的贝叶斯估计策略

IF 1.2 4区综合性期刊 Q3 MULTIDISCIPLINARY SCIENCES

Kuwait Journal of Science Pub Date : 2024-11-13 DOI:10.1016/j.kjs.2024.100339

Nadeem Akhtar , Muteb Faraj Alharthi , Sajjad Ahmad Khan , Akbar Ali Khan , Muhammad Amin

{"title":"双 1 型普查方案下多成分几何寿命测试模型的贝叶斯估计策略","authors":"Nadeem Akhtar , Muteb Faraj Alharthi , Sajjad Ahmad Khan , Akbar Ali Khan , Muhammad Amin","doi":"10.1016/j.kjs.2024.100339","DOIUrl":null,"url":null,"abstract":"<div><div>This study develops a Bayesian approach for estimating the unknown parameters of the 3-component mixture of geometric (3-CMG) model under a doubly type-I censoring scheme <strong>(</strong>DT1CS<strong>)</strong>. The derivations of the Bayes estimators (BEs) and Bayes risks (BRs) are presented under square error loss function (SELF), precautionary loss function (PLF) and DeGroot loss function (DLF) using Beta prior under DT1CS. The strategy is evaluated through extensive simulation and real-life data analysis, showing the strength and efficiency of the newly proposed model. The study recommends that the SELF is the optimal choice for accurately estimating the unknown parameters of the 3-CMG model.</div></div>","PeriodicalId":17848,"journal":{"name":"Kuwait Journal of Science","volume":"52 1","pages":"Article 100339"},"PeriodicalIF":1.2000,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Bayesian estimation strategy for multi-component geometric life testing model under doubly type-1 censoring scheme\",\"authors\":\"Nadeem Akhtar , Muteb Faraj Alharthi , Sajjad Ahmad Khan , Akbar Ali Khan , Muhammad Amin\",\"doi\":\"10.1016/j.kjs.2024.100339\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This study develops a Bayesian approach for estimating the unknown parameters of the 3-component mixture of geometric (3-CMG) model under a doubly type-I censoring scheme <strong>(</strong>DT1CS<strong>)</strong>. The derivations of the Bayes estimators (BEs) and Bayes risks (BRs) are presented under square error loss function (SELF), precautionary loss function (PLF) and DeGroot loss function (DLF) using Beta prior under DT1CS. The strategy is evaluated through extensive simulation and real-life data analysis, showing the strength and efficiency of the newly proposed model. The study recommends that the SELF is the optimal choice for accurately estimating the unknown parameters of the 3-CMG model.</div></div>\",\"PeriodicalId\":17848,\"journal\":{\"name\":\"Kuwait Journal of Science\",\"volume\":\"52 1\",\"pages\":\"Article 100339\"},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2024-11-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Kuwait Journal of Science\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2307410824001640\",\"RegionNum\":4,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Kuwait Journal of Science","FirstCategoryId":"103","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2307410824001640","RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}

引用次数: 0

摘要

本研究开发了一种贝叶斯方法，用于估计双 I 型普查方案（DT1CS）下三分量混合几何模型（3-CMG）的未知参数。在 DT1CS 下使用 Beta 先验的平方误差损失函数（SELF）、预防性损失函数（PLF）和 DeGroot 损失函数（DLF）下，介绍了贝叶斯估计器（BEs）和贝叶斯风险（BRs）的推导。通过大量的模拟和实际数据分析，对该策略进行了评估，显示了新提出模型的优势和效率。研究建议 SELF 是准确估计 3-CMG 模型未知参数的最佳选择。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Bayesian estimation strategy for multi-component geometric life testing model under doubly type-1 censoring scheme

This study develops a Bayesian approach for estimating the unknown parameters of the 3-component mixture of geometric (3-CMG) model under a doubly type-I censoring scheme (DT1CS). The derivations of the Bayes estimators (BEs) and Bayes risks (BRs) are presented under square error loss function (SELF), precautionary loss function (PLF) and DeGroot loss function (DLF) using Beta prior under DT1CS. The strategy is evaluated through extensive simulation and real-life data analysis, showing the strength and efficiency of the newly proposed model. The study recommends that the SELF is the optimal choice for accurately estimating the unknown parameters of the 3-CMG model.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Kuwait Journal of Science MULTIDISCIPLINARY SCIENCES-

CiteScore

1.60

自引率

28.60%

发文量

132

期刊介绍： Kuwait Journal of Science (KJS) is indexed and abstracted by major publishing houses such as Chemical Abstract, Science Citation Index, Current contents, Mathematics Abstract, Micribiological Abstracts etc. KJS publishes peer-review articles in various fields of Science including Mathematics, Computer Science, Physics, Statistics, Biology, Chemistry and Earth & Environmental Sciences. In addition, it also aims to bring the results of scientific research carried out under a variety of intellectual traditions and organizations to the attention of specialized scholarly readership. As such, the publisher expects the submission of original manuscripts which contain analysis and solutions about important theoretical, empirical and normative issues.