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The Energy of Data and Distance Correlation, 数据能量与距离相关性，
IF 2.5 3区 工程技术
Technometrics Pub Date : 2023-07-03 DOI: 10.1080/00401706.2023.2237818
S. Lipovetsky
{"title":"The Energy of Data and Distance Correlation,","authors":"S. Lipovetsky","doi":"10.1080/00401706.2023.2237818","DOIUrl":"https://doi.org/10.1080/00401706.2023.2237818","url":null,"abstract":"This section will review those books whose content and level reflect the general editorial policy of Technometrics. Publishers should send books for review to Ejaz Ahmed, Department of Mathematics and Sciences, Brock University, St. Catharines, ON L2S 3A1 (sahmed5@brocku.ca). The opinions expressed in this section are those of the reviewers. These opinions do not represent positions of the reviewers’ organization and may not reflect those of the editors or the sponsoring societies. Listed prices reflect information provided by the publisher and may not be current. The book purchase programs of the American Society for Quality can provide some of these books at reduced prices for members. For information, contact the American Society for Quality at 1 (800) 248-1946.","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":"65 1","pages":"446 - 448"},"PeriodicalIF":2.5,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48332203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Spatio-Temporal Analysis and Prediction of Mass Telecommunication Base Station Failure Events 大规模通信基站故障事件的时空分析与预测
IF 2.5 3区 工程技术
Technometrics Pub Date : 2023-06-28 DOI: 10.1080/00401706.2023.2231491
Tong Wu, Yudong Wang, Z. Ye, Nan Chen
{"title":"Spatio-Temporal Analysis and Prediction of Mass Telecommunication Base Station Failure Events","authors":"Tong Wu, Yudong Wang, Z. Ye, Nan Chen","doi":"10.1080/00401706.2023.2231491","DOIUrl":"https://doi.org/10.1080/00401706.2023.2231491","url":null,"abstract":"","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48963131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Bayesian modeling and inference for one-shot experiments 单次实验贝叶斯建模与推理
IF 2.5 3区 工程技术
Technometrics Pub Date : 2023-06-14 DOI: 10.1080/00401706.2023.2224524
J. Rougier, Andrew Duncan
{"title":"Bayesian modeling and inference for one-shot experiments","authors":"J. Rougier, Andrew Duncan","doi":"10.1080/00401706.2023.2224524","DOIUrl":"https://doi.org/10.1080/00401706.2023.2224524","url":null,"abstract":"","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45285422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A General Framework for Robust Monitoring of Multivariate Correlated Processes 多元相关过程鲁棒监测的一般框架
IF 2.5 3区 工程技术
Technometrics Pub Date : 2023-06-12 DOI: 10.1080/00401706.2023.2224411
Xiulin Xie, P. Qiu
{"title":"A General Framework for Robust Monitoring of Multivariate Correlated Processes","authors":"Xiulin Xie, P. Qiu","doi":"10.1080/00401706.2023.2224411","DOIUrl":"https://doi.org/10.1080/00401706.2023.2224411","url":null,"abstract":"","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44016168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Sequential Bayesian experimental design for calibration of expensive simulation models 用于校准昂贵仿真模型的序列贝叶斯实验设计
IF 2.5 3区 工程技术
Technometrics Pub Date : 2023-05-25 DOI: 10.1080/00401706.2023.2246157
O. Surer, M. Plumlee, Stefan M. Wild
{"title":"Sequential Bayesian experimental design for calibration of expensive simulation models","authors":"O. Surer, M. Plumlee, Stefan M. Wild","doi":"10.1080/00401706.2023.2246157","DOIUrl":"https://doi.org/10.1080/00401706.2023.2246157","url":null,"abstract":"Simulation models of critical systems often have parameters that need to be calibrated using observed data. For expensive simulation models, calibration is done using an emulator of the simulation model built on simulation output at different parameter settings. Using intelligent and adaptive selection of parameters to build the emulator can drastically improve the efficiency of the calibration process. The article proposes a sequential framework with a novel criterion for parameter selection that targets learning the posterior density of the parameters. The emergent behavior from this criterion is that exploration happens by selecting parameters in uncertain posterior regions while simultaneously exploitation happens by selecting parameters in regions of high posterior density. The advantages of the proposed method are illustrated using several simulation experiments and a nuclear physics reaction model.","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45585917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Fast robust location and scatter estimation: a depth-based method 快速鲁棒定位和散射估计:一种基于深度的方法
IF 2.5 3区 工程技术
Technometrics Pub Date : 2023-05-13 DOI: 10.1080/00401706.2023.2216246
Maoyu Zhang, Yan Song, Wenlin Dai
{"title":"Fast robust location and scatter estimation: a depth-based method","authors":"Maoyu Zhang, Yan Song, Wenlin Dai","doi":"10.1080/00401706.2023.2216246","DOIUrl":"https://doi.org/10.1080/00401706.2023.2216246","url":null,"abstract":"The minimum covariance determinant (MCD) estimator is ubiquitous in multivariate analysis, the critical step of which is to select a subset of a given size with the lowest sample covariance determinant. The concentration step (C-step) is a common tool for subset-seeking; however, it becomes computationally demanding for high-dimensional data. To alleviate the challenge, we propose a depth-based algorithm, termed as texttt{FDB}, which replaces the optimal subset with the trimmed region induced by statistical depth. We show that the depth-based region is consistent with the MCD-based subset under a specific class of depth notions, for instance, the projection depth. With the two suggested depths, the texttt{FDB} estimator is not only computationally more efficient but also reaches the same level of robustness as the MCD estimator. Extensive simulation studies are conducted to assess the empirical performance of our estimators. We also validate the computational efficiency and robustness of our estimators under several typical tasks such as principal component analysis, linear discriminant analysis, image denoise and outlier detection on real-life datasets. A R package textit{FDB} and potential extensions are available in the Supplementary Materials.","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44387606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Sequential Designs for Filling Output Spaces 填充输出空间的顺序设计
IF 2.5 3区 工程技术
Technometrics Pub Date : 2023-05-12 DOI: 10.1080/00401706.2023.2231042
Shangkun Wang, H. Milton, Adam P. Generale, S. Kalidindi, George W. Woodruff, V. Roshan, Joseph H. Milton
{"title":"Sequential Designs for Filling Output Spaces","authors":"Shangkun Wang, H. Milton, Adam P. Generale, S. Kalidindi, George W. Woodruff, V. Roshan, Joseph H. Milton","doi":"10.1080/00401706.2023.2231042","DOIUrl":"https://doi.org/10.1080/00401706.2023.2231042","url":null,"abstract":"Space-filling designs are commonly used in computer experiments to fill the space of inputs so that the input-output relationship can be accurately estimated. However, in certain applications such as inverse design or feature-based modeling, the aim is to fill the response or feature space. In this article, we propose a new experimental design framework that aims to fill the space of the outputs (responses or features). The design is adaptive and model-free, and therefore is expected to be robust to different kinds of modeling choices and input-output relationships. Several examples are given to show the advantages of the proposed method over the traditional input space-filling designs.","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41550936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Constructing a simulation surrogate with partially observed output 构造具有部分观测输出的模拟代理
IF 2.5 3区 工程技术
Technometrics Pub Date : 2023-04-19 DOI: 10.1080/00401706.2023.2210170
Moses Y H Chan, M. Plumlee, Stefan M. Wild
{"title":"Constructing a simulation surrogate with partially observed output","authors":"Moses Y H Chan, M. Plumlee, Stefan M. Wild","doi":"10.1080/00401706.2023.2210170","DOIUrl":"https://doi.org/10.1080/00401706.2023.2210170","url":null,"abstract":"Gaussian process surrogates are a popular alternative to directly using computationally expensive simulation models. When the simulation output consists of many responses, dimension-reduction techniques are often employed to construct these surrogates. However, surrogate methods with dimension reduction generally rely on complete output training data. This article proposes a new Gaussian process surrogate method that permits the use of partially observed output while remaining computationally efficient. The new method involves the imputation of missing values and the adjustment of the covariance matrix used for Gaussian process inference. The resulting surrogate represents the available responses, disregards the missing responses, and provides meaningful uncertainty quantification. The proposed approach is shown to offer sharper inference than alternatives in a simulation study and a case study where an energy density functional model that frequently returns incomplete output is calibrated.","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45216640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Statistical Learning for Nonlinear Dynamical Systems with Applications to Aircraft-UAV Collisions 非线性动力系统的统计学习及其在飞机-无人机碰撞中的应用
IF 2.5 3区 工程技术
Technometrics Pub Date : 2023-04-17 DOI: 10.1080/00401706.2023.2203175
Xinchao Liu, Xiao Liu, T. Kaman, Xiaohua Lu, Guang Lin
{"title":"Statistical Learning for Nonlinear Dynamical Systems with Applications to Aircraft-UAV Collisions","authors":"Xinchao Liu, Xiao Liu, T. Kaman, Xiaohua Lu, Guang Lin","doi":"10.1080/00401706.2023.2203175","DOIUrl":"https://doi.org/10.1080/00401706.2023.2203175","url":null,"abstract":"ABSTRACT This article investigates a physics-informed statistical approach capable of (i) learning nonlinear system dynamics by using data generated from a nonlinear system as well as the underlying governing physics, and (ii) predicting system dynamics with reasonable accuracy and a computational speed much faster than numerical methods. The proposed approach obtains the reduced-order model from the full-order governing equations. A function-to-function regression, based on multivariate Functional Principal Component Analysis, establishes the mapping between external forcing and system dynamics, while a multivariate Gaussian Process is used to capture the relationship between parameters and external forcing. In the application, the proposed approach is applied to predict aircraft nose skin deformation after Unmanned Aerial Vehicle (UAV) collisions at different impact attitudes (i.e., pitch, yaw and roll degrees). We show that the proposed physics-informed statistical model can achieve a 12% out-of-sample mean relative error, and is more than 103 times faster than Finite Element Analysis (FEA). Computer code and sample data are available on GitHub.","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45805941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Penalized estimation of sparse Markov regime-switching vector auto-regressive models 稀疏Markov状态切换向量自回归模型的惩罚估计
IF 2.5 3区 工程技术
Technometrics Pub Date : 2023-04-10 DOI: 10.1080/00401706.2023.2201336
Gilberto Chávez-Martínez, Ankush Agarwal, Abbas Khalili, S. E. Ahmed
{"title":"Penalized estimation of sparse Markov regime-switching vector auto-regressive models","authors":"Gilberto Chávez-Martínez, Ankush Agarwal, Abbas Khalili, S. E. Ahmed","doi":"10.1080/00401706.2023.2201336","DOIUrl":"https://doi.org/10.1080/00401706.2023.2201336","url":null,"abstract":"Abstract We consider sparse Markov regime-switching vector autoregressive (MSVAR) models in which the regimes are governed by a latent homogeneous Markov chain. In practice, even for moderate values of the number of Markovian regimes and data dimension, the associated MSVAR model has a large parameter dimension compared to a typical sample size. We provide a unified penalized conditional likelihood approach for estimating sparse MSVAR models. We show that our proposed estimators are consistent and recover the sparse structure of the model. We also show that, when the number of regimes is correctly or over-specified, our method provides consistent estimation of the predictive density. We develop an efficient implementation of the method based on a modified Expectation-Maximization (EM) algorithm. We discuss strategies for estimation of the number of regimes. We evaluate finite-sample performance of the method via simulations, and further demonstrate its utility by analyzing a real dataset.","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48495269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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