Pub Date : 2023-05-12DOI: 10.1080/00401706.2023.2231042
Shangkun Wang, H. Milton, Adam P. Generale, S. Kalidindi, George W. Woodruff, V. Roshan, Joseph H. Milton
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.
{"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":null,"pages":null},"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}
Pub Date : 2023-04-19DOI: 10.1080/00401706.2023.2210170
Moses Y H Chan, M. Plumlee, Stefan M. Wild
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.
{"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":null,"pages":null},"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}
Pub Date : 2023-04-17DOI: 10.1080/00401706.2023.2203175
Xinchao Liu, Xiao Liu, T. Kaman, Xiaohua Lu, Guang Lin
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.
{"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":null,"pages":null},"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}
Pub Date : 2023-04-10DOI: 10.1080/00401706.2023.2201336
Gilberto Chávez-Martínez, Ankush Agarwal, Abbas Khalili, S. E. Ahmed
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.
{"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":null,"pages":null},"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}
Pub Date : 2023-04-03DOI: 10.1080/00401706.2023.2201126
S. Lipovetsky
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 (dean.fms@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.
{"title":"Introduction to Math Olympiad Problems","authors":"S. Lipovetsky","doi":"10.1080/00401706.2023.2201126","DOIUrl":"https://doi.org/10.1080/00401706.2023.2201126","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 (dean.fms@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":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49518960","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}
Pub Date : 2023-04-03DOI: 10.1080/00401706.2023.2201128
N. Khan
{"title":"The Effect: An Introduction to Research Design and Causality","authors":"N. Khan","doi":"10.1080/00401706.2023.2201128","DOIUrl":"https://doi.org/10.1080/00401706.2023.2201128","url":null,"abstract":"","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46257903","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}
Pub Date : 2023-04-03DOI: 10.1080/00401706.2023.2201132
Firdous Ahmad Mala
{"title":"Number Systems: A Path into Rigorous Mathematics","authors":"Firdous Ahmad Mala","doi":"10.1080/00401706.2023.2201132","DOIUrl":"https://doi.org/10.1080/00401706.2023.2201132","url":null,"abstract":"","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135674705","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}
Pub Date : 2023-04-03DOI: 10.1080/00401706.2023.2201134
S. Lipovetsky
chapter ends with a surprisingly-long account of modular arithmetic. Chapter 6 is devoted to rational numbers, which are the natural and legitimate successors to integers. The chapter discusses the denumerability of rationals after defining rational numbers and explaining their addition and multiplication. An interesting fact proved in the chapter is that rational numbers are countable, which means they are only as many as integers. The chapter concludes with a lovely but succinct presentation of sequences and series. Chapter 7 is about real numbers. It begins with the intriguing question of the completeness of the set of rational numbers and proceeds to state the well-known Axiom of Completeness of Real Numbers. Dedekind cuts are also discussed and demonstrated. The uncountability of reals refers to the fact that there are more reals than rationals. The famous diagonal argument is used in the chapter to demonstrate this fact. Finally, the chapter discusses algebraic and transcendental numbers. Quadratic extensions are discussed in Chapters 8 and 9. The subject matter covered in these two chapters is relatively complex, necessitating greater maturity and concentration on the part of the readers. Chapter 10 attempts to convince readers that the world of numbers is vaster and more immense than they realize. It is divided into two sections: one on constructible numbers and one on hypercomplex numbers, which includes accounts of Hilbert’s quaternions and John Graves’ octonions. Chapter 11 is a two-page guide to which readers should refer for a more comprehensive and advanced understanding of number systems. It implies that, in addition to abstract algebra, analysis is an excellent field for producing more elegant and unexpected results. In all, the book is a great attempt to introduce number systems to an undergraduate audience with the main focus on the rigor of mathematics. While, on the one hand, it provides detailed but accessible explanations of theorems and their proof, on the other hand, it is an attempt to provide the level of explanation needed for a first-year mathematics course on the subject that most others fail to do.
{"title":"Handbook of Measurement Error Models","authors":"S. Lipovetsky","doi":"10.1080/00401706.2023.2201134","DOIUrl":"https://doi.org/10.1080/00401706.2023.2201134","url":null,"abstract":"chapter ends with a surprisingly-long account of modular arithmetic. Chapter 6 is devoted to rational numbers, which are the natural and legitimate successors to integers. The chapter discusses the denumerability of rationals after defining rational numbers and explaining their addition and multiplication. An interesting fact proved in the chapter is that rational numbers are countable, which means they are only as many as integers. The chapter concludes with a lovely but succinct presentation of sequences and series. Chapter 7 is about real numbers. It begins with the intriguing question of the completeness of the set of rational numbers and proceeds to state the well-known Axiom of Completeness of Real Numbers. Dedekind cuts are also discussed and demonstrated. The uncountability of reals refers to the fact that there are more reals than rationals. The famous diagonal argument is used in the chapter to demonstrate this fact. Finally, the chapter discusses algebraic and transcendental numbers. Quadratic extensions are discussed in Chapters 8 and 9. The subject matter covered in these two chapters is relatively complex, necessitating greater maturity and concentration on the part of the readers. Chapter 10 attempts to convince readers that the world of numbers is vaster and more immense than they realize. It is divided into two sections: one on constructible numbers and one on hypercomplex numbers, which includes accounts of Hilbert’s quaternions and John Graves’ octonions. Chapter 11 is a two-page guide to which readers should refer for a more comprehensive and advanced understanding of number systems. It implies that, in addition to abstract algebra, analysis is an excellent field for producing more elegant and unexpected results. In all, the book is a great attempt to introduce number systems to an undergraduate audience with the main focus on the rigor of mathematics. While, on the one hand, it provides detailed but accessible explanations of theorems and their proof, on the other hand, it is an attempt to provide the level of explanation needed for a first-year mathematics course on the subject that most others fail to do.","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46472727","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}
Pub Date : 2023-04-03DOI: 10.1080/00401706.2023.2201129
P. Wludyka
to produce data that we can evaluate with a given method. Simulation can be used to rule out bad estimators and prove the value of good estimators to ourselves. Chapter 16, Fixed Effects, is about fixed effects, random effects, and fixed effects in the non-linear model and regression estimators. Fixed effects are methods of controlling for all variables whether they are observed or not, as long as they stay constant within some layer category. Chapter 17, Event Studies, is about event studies and how they work. The event study is probably the oldest and simplest causal inference research design. Event studies and performed in the stock market, event studies with regression, and event studies with multiple affected groups. Chapter 18, Differences-in-Differences, is about a method, a quasi-experimental approach that is concerned with comparing the changes in outcomes over time between a population enrolled in a program (the treatment group) and a population that is not (the comparison group). Its usefulness in data analysis has already been felt and appreciated. Chapter 19, Instrumental Variables, is about the working of instrumental variables and isolating variation instrumental variables designs seize directly on the concept of randomized control experiment. For work on instrumental variables, we must satisfy two assumptions: relevance of the instrument and validity of the instrument. Chapter 20, Regression Discontinuity, is about regression discontinuity. Regression discontinuity focuses on treatment that is assigned at a cutoff. It also focuses on the concept of running variable or forcing variable, cutoff, bandwidth, regression discontinuity with ordinary least squares, and the density discontinuity test. Chapter 21, A Gallery of Rogues: Other Methods, shows that the world of research design is too wide to be accommodated in a single book. There are methods and designs, both old and new, both tested and untested. This chapter demonstrates that there is a world of other methods that are new and yet developing. Among such exotic methods, the chapter talks about synthetic control, matrix completion, causal discovery, double machine learning, modeling of heterogeneous effects, causal forests, sorted effects, and structural estimation. This chapter could be inaccessible at the first reading, as the content of the chapter is either too advanced or too new to be grasped and understood. Chapter 22, Under the Rug, is all about the assumptions and concerns that are a part of pretty much any causal inference research study, but which often gets ignored or at least brushed aside. Overall, this book, though very voluminous, is an excellent addition to the world of literature. The book contains a good number of examples and wonderfully drawn diagrams, that facilitate a clearer understanding of the concepts. It is a wonderful exhibition of the parts and parcels of research design and causality.
{"title":"Principles of Biostatistics","authors":"P. Wludyka","doi":"10.1080/00401706.2023.2201129","DOIUrl":"https://doi.org/10.1080/00401706.2023.2201129","url":null,"abstract":"to produce data that we can evaluate with a given method. Simulation can be used to rule out bad estimators and prove the value of good estimators to ourselves. Chapter 16, Fixed Effects, is about fixed effects, random effects, and fixed effects in the non-linear model and regression estimators. Fixed effects are methods of controlling for all variables whether they are observed or not, as long as they stay constant within some layer category. Chapter 17, Event Studies, is about event studies and how they work. The event study is probably the oldest and simplest causal inference research design. Event studies and performed in the stock market, event studies with regression, and event studies with multiple affected groups. Chapter 18, Differences-in-Differences, is about a method, a quasi-experimental approach that is concerned with comparing the changes in outcomes over time between a population enrolled in a program (the treatment group) and a population that is not (the comparison group). Its usefulness in data analysis has already been felt and appreciated. Chapter 19, Instrumental Variables, is about the working of instrumental variables and isolating variation instrumental variables designs seize directly on the concept of randomized control experiment. For work on instrumental variables, we must satisfy two assumptions: relevance of the instrument and validity of the instrument. Chapter 20, Regression Discontinuity, is about regression discontinuity. Regression discontinuity focuses on treatment that is assigned at a cutoff. It also focuses on the concept of running variable or forcing variable, cutoff, bandwidth, regression discontinuity with ordinary least squares, and the density discontinuity test. Chapter 21, A Gallery of Rogues: Other Methods, shows that the world of research design is too wide to be accommodated in a single book. There are methods and designs, both old and new, both tested and untested. This chapter demonstrates that there is a world of other methods that are new and yet developing. Among such exotic methods, the chapter talks about synthetic control, matrix completion, causal discovery, double machine learning, modeling of heterogeneous effects, causal forests, sorted effects, and structural estimation. This chapter could be inaccessible at the first reading, as the content of the chapter is either too advanced or too new to be grasped and understood. Chapter 22, Under the Rug, is all about the assumptions and concerns that are a part of pretty much any causal inference research study, but which often gets ignored or at least brushed aside. Overall, this book, though very voluminous, is an excellent addition to the world of literature. The book contains a good number of examples and wonderfully drawn diagrams, that facilitate a clearer understanding of the concepts. It is a wonderful exhibition of the parts and parcels of research design and causality.","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45511296","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}
Pub Date : 2023-03-24DOI: 10.1080/00401706.2023.2190770
B. Weaver, S. V. Wiel
Abstract In some systems lowering any one of several stress variables limits the extent to which the others are able to accelerate random event times. That is, each stress variable can cap acceleration of the time to failure distribution, independent of the others. For example, repeated electrostatic shocks will set off a high-explosive detonator within the first few attempts only if voltage and energy are both sufficiently large. This article presents a class of time-to-event models with soft thresholds on multiple stressors. These models are fit to data obtained from an experiment performed at Los Alamos National Laboratory to estimate probabilities that detonators will fire from accidental electrostatic discharge. The models include a limited failure component to account for the possibility that a fraction of units is completely unable to produce the event of interest regardless of how long one waits or how many trials are attempted.
{"title":"Accelerated Event Times with Multiple Thresholds","authors":"B. Weaver, S. V. Wiel","doi":"10.1080/00401706.2023.2190770","DOIUrl":"https://doi.org/10.1080/00401706.2023.2190770","url":null,"abstract":"Abstract In some systems lowering any one of several stress variables limits the extent to which the others are able to accelerate random event times. That is, each stress variable can cap acceleration of the time to failure distribution, independent of the others. For example, repeated electrostatic shocks will set off a high-explosive detonator within the first few attempts only if voltage and energy are both sufficiently large. This article presents a class of time-to-event models with soft thresholds on multiple stressors. These models are fit to data obtained from an experiment performed at Los Alamos National Laboratory to estimate probabilities that detonators will fire from accidental electrostatic discharge. The models include a limited failure component to account for the possibility that a fraction of units is completely unable to produce the event of interest regardless of how long one waits or how many trials are attempted.","PeriodicalId":22208,"journal":{"name":"Technometrics","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45292129","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}
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