Pub Date : 2023-10-26DOI: 10.1080/00224065.2023.2250884
Zezhong Wang, Inez Maria Zwetsloot
AbstractBecause of the “curse of dimensionality,” high-dimensional processes present challenges to traditional multivariate statistical process monitoring (SPM) techniques. In addition, the unknown underlying distribution of and complicated dependency among variables such as heteroscedasticity increase the uncertainty of estimated parameters and decrease the effectiveness of control charts. In addition, the requirement of sufficient reference samples limits the application of traditional charts in high-dimension, low-sample-size scenarios (small n, large p). More difficulties appear when detecting and diagnosing abnormal behaviors caused by a small set of variables (i.e., sparse changes). In this article, we propose two change-point–based control charts to detect sparse shifts in the mean vector of high-dimensional heteroscedastic processes. Our proposed methods can start monitoring when the number of observations is a lot smaller than the dimensionality. The simulation results show that the proposed methods are robust to nonnormality and heteroscedasticity. Two real data examples are used to illustrate the effectiveness of the proposed control charts in high-dimensional applications. The R codes are provided online.Keywords: change-pointheteroscedasticityhigh-dimensional control chartsparse changesstatistical process monitoring (SPM) Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsZezhong WangZezhong Wang received her doctoral degree from the Department of Systems Engineering, City University of Hong Kong. She is a postdoc researcher at the Hong Kong Industrial Artificial Intelligence and Robotics Centre (FLAIR). Her research interests include statistical process monitoring, data science, and artificial intelligence applications. Her email address is zzwang@hkflair.org.Inez Maria ZwetslootInez M. Zwetsloot is an associate professor in the Department of Business Analytics at the University of Amsterdam, The Netherlands. Previously she was assistant professor at the Department of Systems Engineering, City University of Hong Kong. She is the recipient of the Young Statistician Award (ENBIS, 2021) and the Feigenbaum Medal (ASQ, 2021). Her research focuses on using statistics and analytics for solving business challenges using data. This includes work on statistical process monitoring, network analytics, quality engineering, and data science. Her email address is i.m.zwetsloot@uva.nl.
摘要由于“维度诅咒”的存在，高维过程对传统的多元统计过程监控(SPM)技术提出了挑战。此外，异方差等变量的潜在分布未知和变量间复杂的依赖关系增加了参数估计的不确定性，降低了控制图的有效性。此外，对足够参考样本的要求限制了传统图表在高维、低样本场景(小n，大p)中的应用，当检测和诊断由少量变量(即稀疏变化)引起的异常行为时，会出现更多的困难。在本文中，我们提出了两个基于变化点的控制图来检测高维异方差过程的平均向量的稀疏移位。我们提出的方法可以在观测数远小于维数的情况下开始监测。仿真结果表明，该方法对非正态性和异方差具有较强的鲁棒性。用两个实际数据实例说明了所提出的控制图在高维应用中的有效性。R码在网上提供。关键词:变化点方差高维控制图稀疏变化统计过程监控(SPM)披露声明作者未报告潜在利益冲突。作者简介:王泽忠，博士，毕业于香港城市大学系统工程系。她是香港工业人工智能及机器人中心(FLAIR)的博士后研究员。她的研究兴趣包括统计过程监控、数据科学和人工智能应用。她的电子邮件地址是zzwang@hkflair.org.Inez Maria Zwetsloot inez M. Zwetsloot是荷兰阿姆斯特丹大学商业分析系副教授。她曾任香港城市大学系统工程系助理教授。她是青年统计学家奖(ENBIS, 2021)和费根鲍姆奖章(ASQ, 2021)的获得者。她的研究重点是使用统计和分析来解决使用数据的业务挑战。这包括统计过程监控、网络分析、质量工程和数据科学方面的工作。她的电子邮件地址是i.m.zwetsloot@uva.nl。
{"title":"A change-point–based control chart for detecting sparse mean changes in high-dimensional heteroscedastic data","authors":"Zezhong Wang, Inez Maria Zwetsloot","doi":"10.1080/00224065.2023.2250884","DOIUrl":"https://doi.org/10.1080/00224065.2023.2250884","url":null,"abstract":"AbstractBecause of the “curse of dimensionality,” high-dimensional processes present challenges to traditional multivariate statistical process monitoring (SPM) techniques. In addition, the unknown underlying distribution of and complicated dependency among variables such as heteroscedasticity increase the uncertainty of estimated parameters and decrease the effectiveness of control charts. In addition, the requirement of sufficient reference samples limits the application of traditional charts in high-dimension, low-sample-size scenarios (small n, large p). More difficulties appear when detecting and diagnosing abnormal behaviors caused by a small set of variables (i.e., sparse changes). In this article, we propose two change-point–based control charts to detect sparse shifts in the mean vector of high-dimensional heteroscedastic processes. Our proposed methods can start monitoring when the number of observations is a lot smaller than the dimensionality. The simulation results show that the proposed methods are robust to nonnormality and heteroscedasticity. Two real data examples are used to illustrate the effectiveness of the proposed control charts in high-dimensional applications. The R codes are provided online.Keywords: change-pointheteroscedasticityhigh-dimensional control chartsparse changesstatistical process monitoring (SPM) Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsZezhong WangZezhong Wang received her doctoral degree from the Department of Systems Engineering, City University of Hong Kong. She is a postdoc researcher at the Hong Kong Industrial Artificial Intelligence and Robotics Centre (FLAIR). Her research interests include statistical process monitoring, data science, and artificial intelligence applications. Her email address is zzwang@hkflair.org.Inez Maria ZwetslootInez M. Zwetsloot is an associate professor in the Department of Business Analytics at the University of Amsterdam, The Netherlands. Previously she was assistant professor at the Department of Systems Engineering, City University of Hong Kong. She is the recipient of the Young Statistician Award (ENBIS, 2021) and the Feigenbaum Medal (ASQ, 2021). Her research focuses on using statistics and analytics for solving business challenges using data. This includes work on statistical process monitoring, network analytics, quality engineering, and data science. Her email address is i.m.zwetsloot@uva.nl.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136381710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-13DOI: 10.1080/00224065.2023.2259022
Rakhi Singh
AbstractSupersaturated designs offer cost-effective efficacy in discerning significant factors among a vast array of potential factors, thereby rendering them valuable. The current literature studies several design selection criteria and analysis methods for such designs. For two-level designs, the screening performance of optimal designs constructed under different optimality criteria remains similar, especially when the effect directions are not known in advance. The Gauss-Dantzig Selector (GDS) is the preferred analysis method for two-level designs. For the multi- and mixed-level supersaturated designs, despite the existence of multiple design optimality criteria and design construction methods, the literature lacks guidance for both the design selection and the choice of analysis method. Through extensive simulation studies, we show that the multi- and mixed-level designs constructed using different optimality criteria have equivalent screening performance for the unknown effect directions. For known effect directions, generalized minimum aberration-optimal designs have slightly better screening performance. On the analysis front, however, the story differs from two-level designs. While LASSO and GDS show superior performance among the analysis methods compared, they depend on the parameterization or the coding of factors. Since no single choice of parameterization is best across sparsity levels, scenarios, and designs, we propose using group LASSO, which is invariant to parameterizations. Finally, we characterize the settings in terms of the number of runs, factors, and the effect sparsity, which are too complex to get meaningful results from group LASSO.Keywords: group LASSOmain effectsmixed-levelscreening experimentsthree-level supersaturated design Disclosure statementNo potential conflict of interest was reported by the authors.Data availability statementData availability is not applicable to this article as no new data were created or analyzed in this study.Additional informationNotes on contributorsRakhi SinghDr. Rakhi Singh is an Assistant Professor in the Department of Mathematics and Statistics at Binghamton University in New York, USA. She did her PhD in Mathematics (with specialization in Statistics) under a joint PhD program between Indian Institute of Technology Bombay and Monash University, Australia. She also did a postdoc for a couple of years at TU Dortmund and UNC Greensboro. Her primary research areas are design and analysis of experiments and subdata selection for high-dimensional data.
{"title":"Best practices for multi- and mixed-level supersaturated designs","authors":"Rakhi Singh","doi":"10.1080/00224065.2023.2259022","DOIUrl":"https://doi.org/10.1080/00224065.2023.2259022","url":null,"abstract":"AbstractSupersaturated designs offer cost-effective efficacy in discerning significant factors among a vast array of potential factors, thereby rendering them valuable. The current literature studies several design selection criteria and analysis methods for such designs. For two-level designs, the screening performance of optimal designs constructed under different optimality criteria remains similar, especially when the effect directions are not known in advance. The Gauss-Dantzig Selector (GDS) is the preferred analysis method for two-level designs. For the multi- and mixed-level supersaturated designs, despite the existence of multiple design optimality criteria and design construction methods, the literature lacks guidance for both the design selection and the choice of analysis method. Through extensive simulation studies, we show that the multi- and mixed-level designs constructed using different optimality criteria have equivalent screening performance for the unknown effect directions. For known effect directions, generalized minimum aberration-optimal designs have slightly better screening performance. On the analysis front, however, the story differs from two-level designs. While LASSO and GDS show superior performance among the analysis methods compared, they depend on the parameterization or the coding of factors. Since no single choice of parameterization is best across sparsity levels, scenarios, and designs, we propose using group LASSO, which is invariant to parameterizations. Finally, we characterize the settings in terms of the number of runs, factors, and the effect sparsity, which are too complex to get meaningful results from group LASSO.Keywords: group LASSOmain effectsmixed-levelscreening experimentsthree-level supersaturated design Disclosure statementNo potential conflict of interest was reported by the authors.Data availability statementData availability is not applicable to this article as no new data were created or analyzed in this study.Additional informationNotes on contributorsRakhi SinghDr. Rakhi Singh is an Assistant Professor in the Department of Mathematics and Statistics at Binghamton University in New York, USA. She did her PhD in Mathematics (with specialization in Statistics) under a joint PhD program between Indian Institute of Technology Bombay and Monash University, Australia. She also did a postdoc for a couple of years at TU Dortmund and UNC Greensboro. Her primary research areas are design and analysis of experiments and subdata selection for high-dimensional data.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135857330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-13DOI: 10.1080/00224065.2023.2251178
Yan Wang, Dianpeng Wang, Xiaowei Yue
AbstractThe two-layer computer simulators are commonly used to mimic multi-physics phenomena or systems. Usually, the outputs of the first-layer simulator (also called the inner simulator) are partial inputs of the second-layer simulator (also called the outer simulator). How to design experiments by considering the space-filling properties of inner and outer simulators simultaneously is a significant challenge that has received scant attention in the literature. To address this problem, we propose a new sequential optimal Latin hypercube design (LHD) by using the maximin integrating mixed distance criterion. A corresponding sequential algorithm for efficiently generating such designs is also developed. Numerical simulation results show that the new method can effectively improve the space-filling property of the outer computer inputs. The case study about composite structures assembly simulation demonstrates that the proposed method can outperform the benchmark methods.Keywords: composites structures assembly processesGaussian processmaximin criterionoptimal LHDprincipal component scores AcknowledgmentsWe are grateful to the editor and all reviewers for their excellent work. Their constructive criticism and suggestions significantly improved the quality of this manuscript.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe research of Yan Wang was supported by the National Natural Science Foundation of China (Grant no. NSFC 12101024) and the work of Dianpeng Wang was supported by the National Natural Science Foundation of China (Grant no. NSFC 12171033).Notes on contributorsYan WangYan Wang is an assistant professor at the School of Statistics and Data Science, Beijing University of Technology, Beijing, China.Dianpeng WangDianpeng Wang is an assistant professor at the School of Mathematics and Statistics, and the key laboratory of mathematical theory and computation in information security, Beijing Institute of Technology, Beijing, China.Xiaowei YueXiaowei Yue is an associate professor at the Department of Industrial Engineering, and Institute for Quality and Reliability, Tsinghua University, Beijing, China.
{"title":"Sequential Latin hypercube design for two-layer computer simulators","authors":"Yan Wang, Dianpeng Wang, Xiaowei Yue","doi":"10.1080/00224065.2023.2251178","DOIUrl":"https://doi.org/10.1080/00224065.2023.2251178","url":null,"abstract":"AbstractThe two-layer computer simulators are commonly used to mimic multi-physics phenomena or systems. Usually, the outputs of the first-layer simulator (also called the inner simulator) are partial inputs of the second-layer simulator (also called the outer simulator). How to design experiments by considering the space-filling properties of inner and outer simulators simultaneously is a significant challenge that has received scant attention in the literature. To address this problem, we propose a new sequential optimal Latin hypercube design (LHD) by using the maximin integrating mixed distance criterion. A corresponding sequential algorithm for efficiently generating such designs is also developed. Numerical simulation results show that the new method can effectively improve the space-filling property of the outer computer inputs. The case study about composite structures assembly simulation demonstrates that the proposed method can outperform the benchmark methods.Keywords: composites structures assembly processesGaussian processmaximin criterionoptimal LHDprincipal component scores AcknowledgmentsWe are grateful to the editor and all reviewers for their excellent work. Their constructive criticism and suggestions significantly improved the quality of this manuscript.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe research of Yan Wang was supported by the National Natural Science Foundation of China (Grant no. NSFC 12101024) and the work of Dianpeng Wang was supported by the National Natural Science Foundation of China (Grant no. NSFC 12171033).Notes on contributorsYan WangYan Wang is an assistant professor at the School of Statistics and Data Science, Beijing University of Technology, Beijing, China.Dianpeng WangDianpeng Wang is an assistant professor at the School of Mathematics and Statistics, and the key laboratory of mathematical theory and computation in information security, Beijing Institute of Technology, Beijing, China.Xiaowei YueXiaowei Yue is an associate professor at the Department of Industrial Engineering, and Institute for Quality and Reliability, Tsinghua University, Beijing, China.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135857349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-25DOI: 10.1080/00224065.2023.2246600
Elif Konyar, Mostafa Reisi Gahrooei
AbstractComplex systems are generating more and more high-dimensional data for which tensor analysis showed promising results by capturing complex correlation structures of data. Such data is often distributed among various sites creating challenges for developing data-driven models. Specifically, data privacy and security concerns have been exacerbated in recent years and drove the demand to store and analyze data at the edge of networks rather than sharing it with a centralized server. Federated learning frameworks have been introduced as a solution to these concerns. These frameworks allow local clients to learn local models and collaborate with others to develop a more generalizable aggregated model while handling data privacy issues. In this article, we propose a federated generalized scalar-on-tensor regression framework where multiple local tensor models are learned at the edge, and their parameters are shared with and updated by an aggregator. Experiments on synthetic data sets and two real-world data sets from agriculture and manufacturing domains show the superiority of our approach over several benchmarks.Keywords: aggregated modelfederated learningpersonalized modelscalar-on-tensor regression AcknowledgementsWe would like to thank Ioannis Ampatzidis, Lucas Fideles Costa and Vitor Gontijo da Cunha for providing hyperspectral image data collected at the Southwest Florida Research and Education Center. Also, we would like to thank Massimo Pacella for providing access to the vehicle engine sensor data.Data availability statementThe data used in this article are not publicly available. To request access to the data used in Case Study I (Section 6.1) and Case Study II (Section 6.2), one may contact the corresponding authors of (Costa et al. Citation2022) and (Pacella Citation2018), respectively.Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThis work has been partially supported by the National Science Foundation (NSF) award 2212878.Notes on contributorsElif KonyarElif Konyar is a doctoral student in the Department of Industrial and Systems Engineering at University of Florida. Her email address is elif.konyar@ufl.edu.Mostafa Reisi GahrooeiDr. Mostafa Reisi Gahrooei is an Assistant Professor in the Department of Industrial and Systems Engineering at University of Florida. His email address is mreisigahrooei@ufl.edu. He is the corresponding author.
摘要复杂系统产生了越来越多的高维数据，张量分析通过捕捉数据的复杂关联结构显示出良好的结果。这些数据通常分布在不同的站点中，这给开发数据驱动的模型带来了挑战。具体来说，数据隐私和安全问题近年来已经加剧，并推动了在网络边缘存储和分析数据的需求，而不是与中央服务器共享数据。联邦学习框架的引入是为了解决这些问题。这些框架允许本地客户学习本地模型，并与其他客户协作，在处理数据隐私问题时开发更通用的聚合模型。在本文中，我们提出了一个联邦广义张量标量回归框架，其中在边缘处学习多个局部张量模型，并与聚合器共享和更新它们的参数。在合成数据集和来自农业和制造业领域的两个真实数据集上的实验表明，我们的方法优于几个基准。我们要感谢Ioannis Ampatzidis, Lucas Fideles Costa和Vitor Gontijo da Cunha提供的在西南佛罗里达研究与教育中心收集的高光谱图像数据。同时，我们要感谢Massimo Pacella为我们提供车辆发动机传感器数据。数据可用性声明本文中使用的数据不是公开的。如需访问案例研究I(第6.1节)和案例研究II(第6.2节)中使用的数据，可联系(Costa等人)的通讯作者。Citation2022)和(Pacella Citation2018)。披露声明作者未报告潜在的利益冲突。本研究得到了美国国家科学基金会(NSF) 2212878奖的部分资助。作者selif Konyar是佛罗里达大学工业与系统工程系的一名博士生。她的电子邮件地址是elif.konyar@ufl.edu.Mostafa Reisi GahrooeiDr。Mostafa Reisi Gahrooei是佛罗里达大学工业与系统工程系的助理教授。他的电子邮件地址是mreisigahrooei@ufl.edu。他是通讯作者。
{"title":"Federated generalized scalar-on-tensor regression","authors":"Elif Konyar, Mostafa Reisi Gahrooei","doi":"10.1080/00224065.2023.2246600","DOIUrl":"https://doi.org/10.1080/00224065.2023.2246600","url":null,"abstract":"AbstractComplex systems are generating more and more high-dimensional data for which tensor analysis showed promising results by capturing complex correlation structures of data. Such data is often distributed among various sites creating challenges for developing data-driven models. Specifically, data privacy and security concerns have been exacerbated in recent years and drove the demand to store and analyze data at the edge of networks rather than sharing it with a centralized server. Federated learning frameworks have been introduced as a solution to these concerns. These frameworks allow local clients to learn local models and collaborate with others to develop a more generalizable aggregated model while handling data privacy issues. In this article, we propose a federated generalized scalar-on-tensor regression framework where multiple local tensor models are learned at the edge, and their parameters are shared with and updated by an aggregator. Experiments on synthetic data sets and two real-world data sets from agriculture and manufacturing domains show the superiority of our approach over several benchmarks.Keywords: aggregated modelfederated learningpersonalized modelscalar-on-tensor regression AcknowledgementsWe would like to thank Ioannis Ampatzidis, Lucas Fideles Costa and Vitor Gontijo da Cunha for providing hyperspectral image data collected at the Southwest Florida Research and Education Center. Also, we would like to thank Massimo Pacella for providing access to the vehicle engine sensor data.Data availability statementThe data used in this article are not publicly available. To request access to the data used in Case Study I (Section 6.1) and Case Study II (Section 6.2), one may contact the corresponding authors of (Costa et al. Citation2022) and (Pacella Citation2018), respectively.Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThis work has been partially supported by the National Science Foundation (NSF) award 2212878.Notes on contributorsElif KonyarElif Konyar is a doctoral student in the Department of Industrial and Systems Engineering at University of Florida. Her email address is elif.konyar@ufl.edu.Mostafa Reisi GahrooeiDr. Mostafa Reisi Gahrooei is an Assistant Professor in the Department of Industrial and Systems Engineering at University of Florida. His email address is mreisigahrooei@ufl.edu. He is the corresponding author.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135816361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-21DOI: 10.1080/00224065.2023.2241680
Xia Cai, Li Xu, C. Devon Lin, Yili Hong, Xinwei Deng
AbstractMotivated by the need of finding optimal configuration in the high-performance computing (HPC) system, this work proposes an adaptive-region sequential design (ARSD) for optimization of computer experiments with qualitative and quantitative factors. Experiments with both qualitative and quantitative factors are also encountered in other applications. The proposed ARSD method considers a sequential design criterion under the additive Gaussian process to deal with both qualitative and quantitative factors. Moreover, the adaptiveness of the proposed sequential procedure allows the selection of next design point from the adaptive design region achieving a meaningful balance between exploitation and exploration for optimization. Theoretical justification of the adaptive design region is provided. The performance of the proposed method is evaluated by several numerical examples in simulations. The case study of HPC performance optimization further elaborates the merits of the proposed method.Keywords: Adaptive designdesign of experimentexploitation and explorationGaussian processoptimal configuration AcknowledgementWe are grateful to the editor and the referees for their constructive comments that have helped improve the article significantly.Data availability statementThe data that support the findings of this study are available from the corresponding author upon reasonable request.Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThe work by Cai is supported by the National Natural Science Foundation of China (Grant No. 12001155), and the Natural Science Foundation of Hebei Province of China (Grant No. A2022208001). The work by Lin is supported by the Natural Sciences and Engineering Research Council of Canada.Notes on contributorsXia CaiXia Cai is an Associate Professor in the School of Science at the Hebei University of Science and Technology. Her email address is caixia@hebust.edu.cn.Li XuLi Xu is a PhD candidate in the Department of Statistics at the Virginia Tech. His email address is lix1992@vt.edu.C. Devon LinChunfang Devon Lin is a Professor in the Department of Mathematics and Statistics at the Queen’s University. Her email address is devon.lin@queensu.caYili HongYili Hong is a Professor in the Department of Statistics at the Virginia Tech. His email address is yilihong@vt.edu.Xinwei DengXinwei Deng is a Professor in the Department of Statistics at the Virginia Tech. His email address is xdeng@vt.edu.
{"title":"Adaptive-region sequential design with quantitative and qualitative factors in application to HPC configuration","authors":"Xia Cai, Li Xu, C. Devon Lin, Yili Hong, Xinwei Deng","doi":"10.1080/00224065.2023.2241680","DOIUrl":"https://doi.org/10.1080/00224065.2023.2241680","url":null,"abstract":"AbstractMotivated by the need of finding optimal configuration in the high-performance computing (HPC) system, this work proposes an adaptive-region sequential design (ARSD) for optimization of computer experiments with qualitative and quantitative factors. Experiments with both qualitative and quantitative factors are also encountered in other applications. The proposed ARSD method considers a sequential design criterion under the additive Gaussian process to deal with both qualitative and quantitative factors. Moreover, the adaptiveness of the proposed sequential procedure allows the selection of next design point from the adaptive design region achieving a meaningful balance between exploitation and exploration for optimization. Theoretical justification of the adaptive design region is provided. The performance of the proposed method is evaluated by several numerical examples in simulations. The case study of HPC performance optimization further elaborates the merits of the proposed method.Keywords: Adaptive designdesign of experimentexploitation and explorationGaussian processoptimal configuration AcknowledgementWe are grateful to the editor and the referees for their constructive comments that have helped improve the article significantly.Data availability statementThe data that support the findings of this study are available from the corresponding author upon reasonable request.Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThe work by Cai is supported by the National Natural Science Foundation of China (Grant No. 12001155), and the Natural Science Foundation of Hebei Province of China (Grant No. A2022208001). The work by Lin is supported by the Natural Sciences and Engineering Research Council of Canada.Notes on contributorsXia CaiXia Cai is an Associate Professor in the School of Science at the Hebei University of Science and Technology. Her email address is caixia@hebust.edu.cn.Li XuLi Xu is a PhD candidate in the Department of Statistics at the Virginia Tech. His email address is lix1992@vt.edu.C. Devon LinChunfang Devon Lin is a Professor in the Department of Mathematics and Statistics at the Queen’s University. Her email address is devon.lin@queensu.caYili HongYili Hong is a Professor in the Department of Statistics at the Virginia Tech. His email address is yilihong@vt.edu.Xinwei DengXinwei Deng is a Professor in the Department of Statistics at the Virginia Tech. His email address is xdeng@vt.edu.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136130681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-21DOI: 10.1080/00224065.2023.2249555
Desy Permatasari
"Book Review: Computational and Statistical Methods for Chemical Engineering, 1st edition, by Wim P. Krijnen and Ernst C. Wit, Boca Raton, FL: Chapman & Hall/CRC Press, 2023, 308 pp., $89.95 (eBook); ISBN: 97-81-0031-7819-4.." Journal of Quality Technology, ahead-of-print(ahead-of-print), pp. 1–3 Disclosure statementThe author has reported that there are no potential conflicts of interest.Additional informationFundingThe author thanks the Indonesia Endowment Fund for Education (LPDP) for funding this paper and the authors’ postgraduate studies.
书评:化学工程的计算和统计方法，第一版，Wim P. Krijnen和Ernst C. Wit著，佛罗里达州博卡拉顿:Chapman & Hall/CRC出版社，2023年，308页，89.95美元(电子书);ISBN: 97-81-0031-7819-4…”《质量技术杂志》，印刷前，第1-3页。披露声明作者已报告没有潜在的利益冲突。作者感谢印度尼西亚教育捐赠基金(ldp)为本文和作者的研究生学习提供资金。
{"title":"Book Review: Computational and Statistical Methods for Chemical Engineering, 1st edition, by Wim P. Krijnen and Ernst C. Wit, Boca Raton, FL: Chapman & Hall/CRC Press, 2023, 308 pp., $89.95 (eBook); ISBN: 97-81-0031-7819-4. <b>Computational and Statistical Methods for Chemical Engineering</b> , 1st edition, by Wim P. Krijnen and Ernst C. Wit. Boca Raton, FL: Chapman & Hall/CRC Press, 2023, 308 pp., $89.95 (eBook); ISBN: 97-81-0031-7819-4.","authors":"Desy Permatasari","doi":"10.1080/00224065.2023.2249555","DOIUrl":"https://doi.org/10.1080/00224065.2023.2249555","url":null,"abstract":"\"Book Review: Computational and Statistical Methods for Chemical Engineering, 1st edition, by Wim P. Krijnen and Ernst C. Wit, Boca Raton, FL: Chapman & Hall/CRC Press, 2023, 308 pp., $89.95 (eBook); ISBN: 97-81-0031-7819-4..\" Journal of Quality Technology, ahead-of-print(ahead-of-print), pp. 1–3 Disclosure statementThe author has reported that there are no potential conflicts of interest.Additional informationFundingThe author thanks the Indonesia Endowment Fund for Education (LPDP) for funding this paper and the authors’ postgraduate studies.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136130393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-11DOI: 10.1080/00224065.2023.2249563
Xingyi Yang
{"title":"Statistical Analytics for Health Data Science with SAS and R","authors":"Xingyi Yang","doi":"10.1080/00224065.2023.2249563","DOIUrl":"https://doi.org/10.1080/00224065.2023.2249563","url":null,"abstract":"","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135982364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-19DOI: 10.1080/00224065.2023.2229458
Dengyu Li, Kaibo Wang
{"title":"Multi-node system modeling and monitoring with extended directed graphical models","authors":"Dengyu Li, Kaibo Wang","doi":"10.1080/00224065.2023.2229458","DOIUrl":"https://doi.org/10.1080/00224065.2023.2229458","url":null,"abstract":"","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81342834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-07DOI: 10.1080/00224065.2023.2220973
Yasmeen Akhtar, Fan Zhang, C. Colbourn, J. Stufken, V. Syrotiuk
Abstract Alternative design and analysis methods for screening experiments based on locating arrays are presented. The number of runs in a locating array grows logarithmically based on the number of factors, providing efficient methods for screening complex engineered systems, especially those with large numbers of categorical factors having different numbers of levels. Our analysis method focuses on levels of factors in the identification of important main effects and two-way interactions. We demonstrate the validity of our design and analysis methods on both well-studied and synthetic data sets and investigate both statistical and combinatorial properties of locating arrays that appear to be related to their screening capability.
{"title":"Scalable level-wise screening experiments using locating arrays","authors":"Yasmeen Akhtar, Fan Zhang, C. Colbourn, J. Stufken, V. Syrotiuk","doi":"10.1080/00224065.2023.2220973","DOIUrl":"https://doi.org/10.1080/00224065.2023.2220973","url":null,"abstract":"Abstract Alternative design and analysis methods for screening experiments based on locating arrays are presented. The number of runs in a locating array grows logarithmically based on the number of factors, providing efficient methods for screening complex engineered systems, especially those with large numbers of categorical factors having different numbers of levels. Our analysis method focuses on levels of factors in the identification of important main effects and two-way interactions. We demonstrate the validity of our design and analysis methods on both well-studied and synthetic data sets and investigate both statistical and combinatorial properties of locating arrays that appear to be related to their screening capability.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80751502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-26DOI: 10.1080/00224065.2023.2218622
Oluwagbenga David Agboola
{"title":"ANOVA and Mixed Models: A Short Introduction Using R","authors":"Oluwagbenga David Agboola","doi":"10.1080/00224065.2023.2218622","DOIUrl":"https://doi.org/10.1080/00224065.2023.2218622","url":null,"abstract":"","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72961745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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