Subrata Kundu, Thomas Mazzuchi, Kimberly F. Sellers, Refik Soyer
{"title":"引言:可靠性和风险分析新前沿特刊:致敬Nozer Darabsha Singpurwalla","authors":"Subrata Kundu, Thomas Mazzuchi, Kimberly F. Sellers, Refik Soyer","doi":"10.1002/asmb.70000","DOIUrl":null,"url":null,"abstract":"<p>Nozer D. Singpurwalla (1939–2022)</p><p>We are honored to be guest editors for this special issue of <i>Applied Stochastic Models in Business and Industry</i> which is a tribute to Nozer D. Singpurwalla's scholarly work and achievements. The special issue contains seventeen papers. Four of these papers were based on presented talks at the 2-day conference entitled <i>New Frontiers in Reliability and Risk Analysis</i>, held on October 13–14, 2023 at The George Washington University in Washington, DC. The conference, which was dedicated to Nozer, brought together leading experts and young researchers in the fields in which Nozer was a major contributor, that is reliability, risk analysis, and Bayesian statistics. The special issue includes contributions on these topics from Nozer's friends and colleagues as well as from other researchers.</p><p>The first article by Soyer and Spizzichino presents an overview of Nozer's work in reliability and risk analysis as well as his interests in foundational aspects of statistics, probability, and decision analysis. The paper by Li, Tierney, Hellmayr, and West deals with sequential Bayesian analysis of multivariate time series models with a focus on causal inference which were both areas of interest to Nozer.</p><p>The next two papers are on topics that attracted Nozer's attention due to their foundational implications. Sellers and Booker describe their collaborations with Nozer regarding the connections of fuzzy sets with probability and reliability theory. The authors further discuss subsequent advances in this space and the perceptions across disciplines (particularly among statisticians and data scientists) over the last 20 years. The article by Polson and Sokolov presents an introduction to the notions of negative probability, which was of interest to Nozer during his final years, and the authors give a version of Bayes rule for such probabilities.</p><p>The article by Arkadani, Asadi, and Soofi builds on earlier work by Nozer on the comparison of informativeness of failures versus survivals in life testing. The authors consider a comparison of the information on moments and the model parameters and develop information measures. Finkelstein and Cha present an overview of mixture failure rates (that Nozer often referred to as “predictive failure rate”) to model heterogeneity in reliability and discuss recent developments on the topic including the stochastic intensity paradox.</p><p>The articles by Limnios, and Palayangoda and Balakrishnan deal with gamma processes for degradation modeling. Nozer used the gamma process in his study of Bayesian life testing, and failure processes in dynamic and multiple failure mode environments. Limnios considers a gamma process for degradation under a random environment modeled by a Markov process and presents results for averaging and normal deviation. Palayangoda and Balakrishnan consider a complete likelihood for the gamma processes and develop inference using the EM algorithm.</p><p>Lindqvist and Taraldsen present a data-generating function-based approach for simulating exact confidence intervals for reliability and discuss the connection with fiducial inference. Equivalence results are presented for confidence bands for fatigue-life and fatigue-strength models in the article by Liu, Hong, Escobar, and Meeker. The equivalence results are shown for quantile and cumulative distribution functions. Kim and Wilson consider reliability demonstration tests and present a Bayesian approach to identify a set of binomial test plans by taking into account posterior consumer and producer risks.</p><p>The next two articles deal with system reliability modeling. Lei and Kuo utilize order statistics associated with unit failure times to simplify and make more efficient system reliability calculations. Joint probability distributions for multi-state series and parallel systems with independent components are obtained in Kulkarni, Sabnis, and Ghosh and they are compared with the probability functions from the respective binary versions.</p><p>Motivated by queueing, Sethuraman considers systems that are subject to interruptions, such as power outages, that cause re-starting of the service provided by the system. Asymptotic results are obtained for a stochastic process, with independent increments, based on “time to complete a service.” Stochastic ordering properties and identifiability issues in latent activation failure models are discussed by Jiang and Basu who consider latent fixed order statistics models as well as hierarchical activation models. The next paper is by Cui, Li, Wan, and Zhang who use model averaging and a jackknife-based weight selection criterion to estimate the conditional average treatment effect in binary response models.</p><p>The final article by Misaii et al. presents a comparison of the predictive performance of statistical and AI/ML models in the analysis of degradation data and provides insights on different modeling approaches using a case study.</p><p>This special issue not only serves to honor Nozer's contributions and legacy—it further advances research in the fields of reliability, risk, and Bayesian analysis.</p><p>The authors declare no conflicts of interest.</p>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 1","pages":""},"PeriodicalIF":1.3000,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.70000","citationCount":"0","resultStr":"{\"title\":\"Foreword Special Issue on New Frontiers in Reliability and Risk Analysis: A Tribute to Nozer Darabsha Singpurwalla\",\"authors\":\"Subrata Kundu, Thomas Mazzuchi, Kimberly F. 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The special issue includes contributions on these topics from Nozer's friends and colleagues as well as from other researchers.</p><p>The first article by Soyer and Spizzichino presents an overview of Nozer's work in reliability and risk analysis as well as his interests in foundational aspects of statistics, probability, and decision analysis. The paper by Li, Tierney, Hellmayr, and West deals with sequential Bayesian analysis of multivariate time series models with a focus on causal inference which were both areas of interest to Nozer.</p><p>The next two papers are on topics that attracted Nozer's attention due to their foundational implications. Sellers and Booker describe their collaborations with Nozer regarding the connections of fuzzy sets with probability and reliability theory. The authors further discuss subsequent advances in this space and the perceptions across disciplines (particularly among statisticians and data scientists) over the last 20 years. The article by Polson and Sokolov presents an introduction to the notions of negative probability, which was of interest to Nozer during his final years, and the authors give a version of Bayes rule for such probabilities.</p><p>The article by Arkadani, Asadi, and Soofi builds on earlier work by Nozer on the comparison of informativeness of failures versus survivals in life testing. The authors consider a comparison of the information on moments and the model parameters and develop information measures. Finkelstein and Cha present an overview of mixture failure rates (that Nozer often referred to as “predictive failure rate”) to model heterogeneity in reliability and discuss recent developments on the topic including the stochastic intensity paradox.</p><p>The articles by Limnios, and Palayangoda and Balakrishnan deal with gamma processes for degradation modeling. Nozer used the gamma process in his study of Bayesian life testing, and failure processes in dynamic and multiple failure mode environments. Limnios considers a gamma process for degradation under a random environment modeled by a Markov process and presents results for averaging and normal deviation. Palayangoda and Balakrishnan consider a complete likelihood for the gamma processes and develop inference using the EM algorithm.</p><p>Lindqvist and Taraldsen present a data-generating function-based approach for simulating exact confidence intervals for reliability and discuss the connection with fiducial inference. Equivalence results are presented for confidence bands for fatigue-life and fatigue-strength models in the article by Liu, Hong, Escobar, and Meeker. The equivalence results are shown for quantile and cumulative distribution functions. Kim and Wilson consider reliability demonstration tests and present a Bayesian approach to identify a set of binomial test plans by taking into account posterior consumer and producer risks.</p><p>The next two articles deal with system reliability modeling. Lei and Kuo utilize order statistics associated with unit failure times to simplify and make more efficient system reliability calculations. Joint probability distributions for multi-state series and parallel systems with independent components are obtained in Kulkarni, Sabnis, and Ghosh and they are compared with the probability functions from the respective binary versions.</p><p>Motivated by queueing, Sethuraman considers systems that are subject to interruptions, such as power outages, that cause re-starting of the service provided by the system. Asymptotic results are obtained for a stochastic process, with independent increments, based on “time to complete a service.” Stochastic ordering properties and identifiability issues in latent activation failure models are discussed by Jiang and Basu who consider latent fixed order statistics models as well as hierarchical activation models. 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引用次数: 0
摘要
Nozer D. Singpurwalla(1935 - 2022)我们很荣幸成为《商业和工业中的应用随机模型》特刊的客座编辑,这是对Nozer D. Singpurwalla的学术工作和成就的致敬。这期特刊有17篇论文。其中四篇论文是基于2023年10月13日至14日在华盛顿特区的乔治华盛顿大学举行的为期两天的题为“可靠性和风险分析新领域”会议上的演讲。这次会议以诺泽尔为主题,汇集了诺泽尔在可靠性、风险分析和贝叶斯统计等领域的主要专家和年轻研究人员。这期特刊包括Nozer的朋友和同事以及其他研究人员对这些主题的贡献。Soyer和Spizzichino的第一篇文章概述了Nozer在可靠性和风险分析方面的工作,以及他对统计、概率和决策分析的基础方面的兴趣。Li, Tierney, Hellmayr和West的论文处理多变量时间序列模型的顺序贝叶斯分析,重点是因果推理,这两个领域都是Nozer感兴趣的。接下来的两篇论文由于其基本含义而引起了Nozer的注意。Sellers和Booker描述了他们与Nozer在概率和可靠性理论中关于模糊集连接的合作。作者进一步讨论了这一领域的后续进展,以及过去20年来跨学科(特别是统计学家和数据科学家)的看法。Polson和Sokolov的文章介绍了负概率的概念,这是Nozer在他的最后几年感兴趣的,作者给出了这种概率的贝叶斯规则版本。Arkadani, Asadi和Soofi的文章建立在Nozer早期关于寿命测试中故障与存活的信息性比较的工作的基础上。作者考虑了力矩信息与模型参数的比较,并提出了信息度量方法。Finkelstein和Cha概述了混合故障率(Nozer通常称之为“预测故障率”),以模拟可靠性中的异质性,并讨论了该主题的最新发展,包括随机强度悖论。Limnios、Palayangoda和Balakrishnan的文章涉及退化建模的伽马过程。Nozer在他的研究贝叶斯寿命测试和动态和多失效模式环境下的失效过程中使用了伽马过程。Limnios考虑了一个用马尔可夫过程建模的随机环境下退化的伽马过程,并给出了平均和正态偏差的结果。Palayangoda和Balakrishnan考虑了伽马过程的完全似然性,并使用EM算法开发了推理。Lindqvist和Taraldsen提出了一种基于数据生成函数的方法来模拟可靠性的精确置信区间,并讨论了与基准推理的联系。Liu, Hong, Escobar和Meeker的文章给出了疲劳寿命和疲劳强度模型置信带的等效结果。对于分位数分布函数和累积分布函数,给出了等效结果。Kim和Wilson考虑了可靠性演示测试,并提出了一种贝叶斯方法,通过考虑后验消费者和生产者风险来识别一组二项测试计划。接下来的两篇文章将讨论系统可靠性建模。Lei和Kuo利用与单元故障时间相关的顺序统计来简化和更有效地进行系统可靠性计算。在Kulkarni、Sabnis和Ghosh中分别得到了具有独立分量的多态串联和并联系统的联合概率分布,并将其与各自二进制版本的概率函数进行了比较。在排队的激励下,Sethuraman考虑了受中断(如停电)影响的系统,这会导致系统提供的服务重新启动。对于基于“完成服务的时间”的随机过程,得到了具有独立增量的渐近结果。Jiang和Basu讨论了潜在激活失效模型的随机有序特性和可识别性问题,他们考虑了潜在的固定顺序统计模型和分层激活模型。下一篇论文由Cui、Li、Wan和Zhang撰写,他们使用模型平均和基于jackknife的权重选择准则来估计二元响应模型中的条件平均处理效果。Misaii等人的最后一篇文章比较了统计模型和AI/ML模型在退化数据分析中的预测性能,并通过案例研究提供了不同建模方法的见解。 这期特刊不仅是为了纪念Nozer的贡献和遗产——它进一步推进了可靠性、风险和贝叶斯分析领域的研究。作者声明无利益冲突。
Foreword Special Issue on New Frontiers in Reliability and Risk Analysis: A Tribute to Nozer Darabsha Singpurwalla
Nozer D. Singpurwalla (1939–2022)
We are honored to be guest editors for this special issue of Applied Stochastic Models in Business and Industry which is a tribute to Nozer D. Singpurwalla's scholarly work and achievements. The special issue contains seventeen papers. Four of these papers were based on presented talks at the 2-day conference entitled New Frontiers in Reliability and Risk Analysis, held on October 13–14, 2023 at The George Washington University in Washington, DC. The conference, which was dedicated to Nozer, brought together leading experts and young researchers in the fields in which Nozer was a major contributor, that is reliability, risk analysis, and Bayesian statistics. The special issue includes contributions on these topics from Nozer's friends and colleagues as well as from other researchers.
The first article by Soyer and Spizzichino presents an overview of Nozer's work in reliability and risk analysis as well as his interests in foundational aspects of statistics, probability, and decision analysis. The paper by Li, Tierney, Hellmayr, and West deals with sequential Bayesian analysis of multivariate time series models with a focus on causal inference which were both areas of interest to Nozer.
The next two papers are on topics that attracted Nozer's attention due to their foundational implications. Sellers and Booker describe their collaborations with Nozer regarding the connections of fuzzy sets with probability and reliability theory. The authors further discuss subsequent advances in this space and the perceptions across disciplines (particularly among statisticians and data scientists) over the last 20 years. The article by Polson and Sokolov presents an introduction to the notions of negative probability, which was of interest to Nozer during his final years, and the authors give a version of Bayes rule for such probabilities.
The article by Arkadani, Asadi, and Soofi builds on earlier work by Nozer on the comparison of informativeness of failures versus survivals in life testing. The authors consider a comparison of the information on moments and the model parameters and develop information measures. Finkelstein and Cha present an overview of mixture failure rates (that Nozer often referred to as “predictive failure rate”) to model heterogeneity in reliability and discuss recent developments on the topic including the stochastic intensity paradox.
The articles by Limnios, and Palayangoda and Balakrishnan deal with gamma processes for degradation modeling. Nozer used the gamma process in his study of Bayesian life testing, and failure processes in dynamic and multiple failure mode environments. Limnios considers a gamma process for degradation under a random environment modeled by a Markov process and presents results for averaging and normal deviation. Palayangoda and Balakrishnan consider a complete likelihood for the gamma processes and develop inference using the EM algorithm.
Lindqvist and Taraldsen present a data-generating function-based approach for simulating exact confidence intervals for reliability and discuss the connection with fiducial inference. Equivalence results are presented for confidence bands for fatigue-life and fatigue-strength models in the article by Liu, Hong, Escobar, and Meeker. The equivalence results are shown for quantile and cumulative distribution functions. Kim and Wilson consider reliability demonstration tests and present a Bayesian approach to identify a set of binomial test plans by taking into account posterior consumer and producer risks.
The next two articles deal with system reliability modeling. Lei and Kuo utilize order statistics associated with unit failure times to simplify and make more efficient system reliability calculations. Joint probability distributions for multi-state series and parallel systems with independent components are obtained in Kulkarni, Sabnis, and Ghosh and they are compared with the probability functions from the respective binary versions.
Motivated by queueing, Sethuraman considers systems that are subject to interruptions, such as power outages, that cause re-starting of the service provided by the system. Asymptotic results are obtained for a stochastic process, with independent increments, based on “time to complete a service.” Stochastic ordering properties and identifiability issues in latent activation failure models are discussed by Jiang and Basu who consider latent fixed order statistics models as well as hierarchical activation models. The next paper is by Cui, Li, Wan, and Zhang who use model averaging and a jackknife-based weight selection criterion to estimate the conditional average treatment effect in binary response models.
The final article by Misaii et al. presents a comparison of the predictive performance of statistical and AI/ML models in the analysis of degradation data and provides insights on different modeling approaches using a case study.
This special issue not only serves to honor Nozer's contributions and legacy—it further advances research in the fields of reliability, risk, and Bayesian analysis.
期刊介绍:
ASMBI - Applied Stochastic Models in Business and Industry (formerly Applied Stochastic Models and Data Analysis) was first published in 1985, publishing contributions in the interface between stochastic modelling, data analysis and their applications in business, finance, insurance, management and production. In 2007 ASMBI became the official journal of the International Society for Business and Industrial Statistics (www.isbis.org). The main objective is to publish papers, both technical and practical, presenting new results which solve real-life problems or have great potential in doing so. Mathematical rigour, innovative stochastic modelling and sound applications are the key ingredients of papers to be published, after a very selective review process.
The journal is very open to new ideas, like Data Science and Big Data stemming from problems in business and industry or uncertainty quantification in engineering, as well as more traditional ones, like reliability, quality control, design of experiments, managerial processes, supply chains and inventories, insurance, econometrics, financial modelling (provided the papers are related to real problems). The journal is interested also in papers addressing the effects of business and industrial decisions on the environment, healthcare, social life. State-of-the art computational methods are very welcome as well, when combined with sound applications and innovative models.