Estimation of P(Y IF 2.3 2区 工程技术 Q3 ENGINEERING, INDUSTRIAL Quality Technology and Quantitative Management Pub Date : 2022-05-04 DOI:10.1080/16843703.2022.2052585 Y. Tripathi, C. Petropoulos, M. Jha 求助PDF {"title":"Estimation of P(Y<X) for lognormal distribution","authors":"Y. Tripathi, C. Petropoulos, M. Jha","doi":"10.1080/16843703.2022.2052585","DOIUrl":null,"url":null,"abstract":"ABSTRACT The problem of estimating a stress-strength parameter is considered under the assumption that both stress and strength variables follow independent lognormal distributions. The maximum likelihood estimator of this parametric function is obtained and based on its asymptotic distribution, approximate intervals are constructed. Properties of a heuristic estimator are studied as well. Bootstrap intervals are also constructed. Bayes estimator is derived against proper and improper prior distributions. We further compute credible intervals of stress–strength parameter using importance sampling. The empirical Bayes procedure is also discussed. Proposed point and interval estimators are compared numerically using Monte Carlo simulations. Analysis of a real data set is presented for illustration purposes.","PeriodicalId":49133,"journal":{"name":"Quality Technology and Quantitative Management","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2022-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Quality Technology and Quantitative Management","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/16843703.2022.2052585","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0} 引用次数: 2 引用 批量引用 Abstract ABSTRACT The problem of estimating a stress-strength parameter is considered under the assumption that both stress and strength variables follow independent lognormal distributions. The maximum likelihood estimator of this parametric function is obtained and based on its asymptotic distribution, approximate intervals are constructed. Properties of a heuristic estimator are studied as well. Bootstrap intervals are also constructed. Bayes estimator is derived against proper and improper prior distributions. We further compute credible intervals of stress–strength parameter using importance sampling. The empirical Bayes procedure is also discussed. Proposed point and interval estimators are compared numerically using Monte Carlo simulations. Analysis of a real data set is presented for illustration purposes. 分享 查看原文 本刊更多论文 对数正态分布中P（Y 在假定应力和强度变量均服从独立对数正态分布的情况下，研究了应力-强度参数的估计问题。得到了该参数函数的极大似然估计量，并根据其渐近分布构造了近似区间。研究了启发式估计器的性质。还构造了引导区间。针对合适和不合适的先验分布分别导出了贝叶斯估计量。进一步利用重要抽样法计算应力-强度参数的可信区间。本文还讨论了经验贝叶斯方法。用蒙特卡罗模拟对所提出的点估计和区间估计进行了数值比较。为了说明目的，本文给出了一个真实数据集的分析。 本文章由计算机程序翻译，如有差异，请以英文原文为准。 求助全文 约1分钟内获得全文 求助全文 来源期刊 Quality Technology and Quantitative Management ENGINEERING, INDUSTRIAL-OPERATIONS RESEARCH & MANAGEMENT SCIENCE CiteScore 5.10 自引率 21.40% 发文量 47 审稿时长 >12 weeks 期刊介绍： Quality Technology and Quantitative Management is an international refereed journal publishing original work in quality, reliability, queuing service systems, applied statistics (including methodology, data analysis, simulation), and their applications in business and industrial management. 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Abstract

ABSTRACT The problem of estimating a stress-strength parameter is considered under the assumption that both stress and strength variables follow independent lognormal distributions. The maximum likelihood estimator of this parametric function is obtained and based on its asymptotic distribution, approximate intervals are constructed. Properties of a heuristic estimator are studied as well. Bootstrap intervals are also constructed. Bayes estimator is derived against proper and improper prior distributions. We further compute credible intervals of stress–strength parameter using importance sampling. The empirical Bayes procedure is also discussed. Proposed point and interval estimators are compared numerically using Monte Carlo simulations. Analysis of a real data set is presented for illustration purposes.