Showing papers in "Technometrics in 2020"
TL;DR: The first edition of this book was released in 2013 although I do not see a record of a review in a previous edition of Technometrics as discussed by the authors. Like the first edition, the aim and scope remain unchanged and...
Abstract: The first edition of this book was released in 2013 although I do not see a record of a review in a previous edition of Technometrics. Like the first edition, the aim and scope remain unchanged and...
285 citations
TL;DR: In this article, a Gaussian Process (GP) method for handling both qualitative and numerical inputs is proposed. But this method assumes a different response surface for each combination of inputs.
Abstract: Computer simulations often involve both qualitative and numerical inputs. Existing Gaussian process (GP) methods for handling this mainly assume a different response surface for each combination of...
69 citations
TL;DR: A novel control chart is proposed that makes use of the restarting mechanism of a CUSUM chart and the related spring length concept and ignores all history data that are beyond the spring length of the current time point.
Abstract: –Statistical process control (SPC) charts are critically important for quality control and management in manufacturing industries, environmental monitoring, disease surveillance, and many o...
34 citations
TL;DR: The proposed procedure overcomes the limitations of conventional diagnostic procedures by controlling the wMDR and minimizing the expected number of false positives as well and it is shown theoretically that the proposed procedure is asymptotically valid and optimal in a certain sense.
Abstract: Monitoring complex systems involving high-dimensional data streams (HDS) provides quick real-time detection of abnormal changes of system performance, but accurate and efficient diagnosis of the st...
31 citations
TL;DR: Ridge or more formally l2 regularization shows up in many areas of statistics and machine learning It is one of those essential devices that any good data scientist needs to master for their craft.
Abstract: Ridge or more formally l2 regularization shows up in many areas of statistics and machine learning It is one of those essential devices that any good data scientist needs to master for their craft
28 citations
TL;DR: This work generalizes PCA to handle various types of data using the generalized linear model framework, and provides low-rank estimates of the natural parameters by projecting the saturated model parameters.
Abstract: Principal component analysis (PCA) is very useful for a wide variety of data analysis tasks, but its implicit connection to the Gaussian distribution can be undesirable for discrete data such as bi...
28 citations
TL;DR: The monograph presents a great introduction to data science and modern R programing, with tons of examples of application of the R abilities throughout the whole volume.
Abstract: The monograph belongs to the series Texts in Statistical Science and presents the sixth upgraded edition of the popular manual. It was first issued in 1984, and from that time won recognition as on...
25 citations
TL;DR: A multivariate Wiener process is first used to model the correlation among different dimensions of degradation, and an expectation-maximization algorithm is developed to obtain the point estimates of the model parameters and construct confidence intervals for the parameters.
Abstract: In degradation tests, the test units are usually divided into several groups, with each group tested simultaneously in a test rig. Each rig constitutes a rig-layer block from the perspective of des...
25 citations
TL;DR: The purpose of this article is to provide historical context by discussing the men involved, their work at DuPont, and their approach to methodological development in the context of these classic articles on Ridge Regression.
Abstract: Two classical articles on Ridge Regression by Arthur Hoerl and Robert Kennard were published in Technometrics in 1970, making 2020 their 50th anniversary. The theory and practice of Ridge Regressio...
21 citations
21 citations
TL;DR: The second edition of this book by Meeker, Hahn, and Escobar is out with an extensive revision and substantial extensions to include more modern computational-driven techniques as well as up-to-date computing resources for calculating statistical intervals.
Abstract: The ability to quantifying certainty of an estimated or predicted quantity is key to practical and realistic decision-making. Hence, realistic and correct statistical intervals are essential to all...
TL;DR: A novel linear discriminant analysis approach for the classification of high-dimensional matrix-valued data that commonly arises from imaging studies is proposed using an efficient nuclear norm penalized regression that encourages a low-rank structure.
Abstract: We propose a novel linear discriminant analysis (LDA) approach for the classification of high-dimensional matrix-valued data that commonly arises from imaging studies. Motivated by the equivalence ...
TL;DR: A multilayer weighted stochastic block model based on a multivariate zero-inflated Poisson (MZIP) distribution to characterize the sparse and correlated multILayer interactions of individuals and a monitoring statistic based on the score test of MZIP-SBM model parameters for change detection in multilayers networks is proposed.
Abstract: In a typical network with a set of individuals, it is common to have multiple types of interactions between two individuals. In practice, these interactions are usually sparse and correlated, which...
TL;DR: This work defines a new family of orthogonal RSDs, for which there is no aliasing between the main effects and the second-order effects (two-factor interactions and quadratic effects), and presents a multiattribute decision algorithm to select designs from the catalog.
Abstract: Response surface designs (RSDs) are a core component of the response surface methodology, which is widely used in the context of product and process optimization. In this contribution, we c...
TL;DR: Using a periodic metric to guarantee the statistical uniformity of the family of distance-based designs is proposed, which forces univariate projections to be uniform and improves accuracy in Monte Carlo integration of some functions.
Abstract: This article proposes a sampling technique that delivers robust designs, that is, point sets selected from a design domain in the shape of a unit hypercube. The designs are guaranteed to provide a ...
TL;DR: The book is innovative even for specialists in game theory and operations research, decision making and applied socioeconomics research in various fields, and in practical implementations SV has been successfully applied in marketing research.
Abstract: The textbook belongs to the Data Science series and presents a modern approach to statistical evaluations via powerful abilities of the R language. The monograph is organized in six parts and thirt...
TL;DR: Raman mapping technique has been used to perform in-line quality inspections of nanomanufacturing processes as mentioned in this paper, and massive high-dimensional Raman mapping data with mixed effects have been used.
Abstract: Raman mapping technique has been used to perform in-line quality inspections of nanomanufacturing processes. In such an application, massive high-dimensional Raman mapping data with mixed effects i...
TL;DR: A novel and effective new method for DEDAP, where a patient’s risk to the disease is first quantified at each time point, and then the longitudinal pattern of the risk is monitored sequentially over time, works well in practice.
Abstract: Many diseases can be prevented or treated if they can be detected early or signaled before their occurrence. Disease early detection and prevention (DEDAP) is thus important for health improvement ...
TL;DR: This work argues that transformation of the response can be used for making the deterministic function approximately additive, which can then be easily estimated using an additive GP, and proposes an extension of the TAG process called transformed approximately additive Gaussian (TAAG) process.
Abstract: We discuss the problem of approximating a deterministic function using Gaussian processes (GPs). The role of transformation in GP modeling is not well understood. We argue that transformation of th...
TL;DR: In this article, the authors highlight the recent research on diagnostic meta-analysis and related methods for the systematic synthesis of diagnostic test accuracy studies in a host of applications, including medical applications.
Abstract: This edited volume showcases the recent research on diagnostic meta-analysis and related methods for the systematic synthesis of diagnostic test accuracy studies in a host of applications. Meta-ana...
TL;DR: The book describes how a global null-hypothesis can be constructed, test statistics performed, sampling distributions and p-values estimated, and working with the R-package npvm is described, with examples of its application and interpretation of the results.
Abstract: This edited volume showcases the research on sequence analysis and related methods for analyzing longitudinal data in a host of applications. The longitudinal data analysis has been useful and popu...
TL;DR: A new performance evaluation approach is proposed, called process monitoring receiver operating characteristic curve, which properly combines the signal times with (FPR,FNR) and shows that this approach provides an effective tool for measuring the performance of DS methods.
Abstract: In practice, we often need to sequentially monitor the performance of individual subjects or processes, so that interventions can be made in a timely manner to avoid unpleasant consequences (e.g., ...
TL;DR: In this article, an approach for fitting linear regression models that splits the set of covariates into groups was proposed, and the optimal split of the variables into groups and the regularized estimation of the regre...
Abstract: We propose an approach for fitting linear regression models that splits the set of covariates into groups. The optimal split of the variables into groups and the regularized estimation of the regre...
TL;DR: In employing a filter algorithm for solving constrained optimization problems, a novel probabilistic metric is established for guiding the filter in order to quickly converge to a global solution to the constrained optimization problem.
Abstract: Expensive black box systems arise in many engineering applications but can be difficult to optimize because their output functions may be complex, multi-modal, and difficult to understand. The task...
TL;DR: A new method for constructing supersaturated designs that is based on the Kronecker product of two carefully chosen matrices is proposed, which leads to a partitioning of the factors of the design such that the factors within a group are correlated to the others within the same group, but are orthogonal to any factor in any other group.
Abstract: In this article, we propose a new method for constructing supersaturated designs that is based on the Kronecker product of two carefully chosen matrices. The construction method leads to a partitio...
TL;DR: This book is a very reader-friendly written, helping to students and researchers in various fields to understand what for a statistical tool can serve, how to apply it, and to interpret computer outputs.
Abstract: The monograph belongs to the The R series, and it can serve as a convenient way for learning data science and statistics simultaneously with the R language. The textbook consists of four parts, ele...
TL;DR: A novel “sliced effect hierarchy principle” is introduced and design criteria to generate factorial designs for multi-platform experiments are developed and a theorem is proved that connects the proposed designs to the well-known minimum aberration designs.
Abstract: Multivariate testing is a popular method to improve websites, mobile apps, and email campaigns. A unique aspect of testing in the online space is that it needs to be conducted across multiple platf...
TL;DR: It is shown that certain statistical properties of the resulting experimental design depend on the exact columns dropped and that other properties are insensitive to these columns.
Abstract: –Definitive screening designs permit the study of many quantitative factors in a few runs more than twice the number of factors. In practical applications, researchers often require a desig...
TL;DR: A unified algorithm to perform sparse learning of fused insurance data under the Tweedie (compound Poisson) model is proposed, which clearly outperforms single-target modeling in both prediction and selection accuracy, notably when the sources do not have exactly the same set of predictors.
Abstract: Actuarial practitioners now have access to multiple sources of insurance data corresponding to various situations: multiple business lines, umbrella coverage, multiple hazards, and so on. Despite t...
TL;DR: A robust statistical model using a Student-t process to assess the lifetime information of highly reliable products is proposed, which is statistically plausible and demonstrates a substantially improved fit when applied to real data.
Abstract: Stochastic processes are widely used to analyze degradation data, and the Gaussian process is a particularly common one. In this article, we propose a robust statistical model using a Student-t pro...