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...
308 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...
110 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
64 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...
59 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...
58 citations
TL;DR: Fletcher and Fortin’s book is an extremely valuable introduction to spatial ecology and both basic and more advanced methodological tools to conduct spatial analyses in R.
Abstract: The last few decades of ecological research were marked by a growing recognition of the importance of space in shaping ecological patterns and processes. This is not only reflected by the emergence of subfields like landscape ecology and metapopulation/ metacommunity, but also by the development of an array of statistical methods and models for spatial analysis. Such progress has resulted in the release of several books, software, and packages on how to perform spatial statistics in a vast range of disciplines ranging from environmental studies and conservation biology to ecology, geography, and landscape ecology. Nonetheless, the study of spatial processes and their ecological consequences remains an intricate task and the growing number of statistical tools to do so can be overwhelming. By linking spatial ecology concepts with spatial statistics approaches, Spatial Ecology and Conservation Modeling: Applications with R by Robert Fletcher and Marie-Josée Fortin provides an overview of the issues often faced by ecologists and conservation practitioners when dealing with spatial analysis. The book will help scientists and practitioners learn the right tools to conduct their research, identify the challenges they face with their datasets, and circumvent those challenges by linking spatial analysis to ecological processes. As such, Fletcher and Fortin’s book is an extremely valuable introduction to spatial ecology and both basic and more advanced methodological tools to conduct spatial analyses in R.
40 citations
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...
35 citations
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...
34 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...
33 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...
32 citations
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: 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: 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: 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 this article, the authors provide mathematical completeness, computational illustration and implementation, and conciseness and accessibility to the reader in an introductory time series book, which is based on a time series.
Abstract: The trio of goals for this introductory time series book is to provide (1) mathematical completeness, (2) computational illustration and implementation, and (3) conciseness and accessibility to upp...
TL;DR: This article investigates a statistical approach which integrates the random forests algorithm and the classical data analysis methodologies for repairable system reliability, such as the nonparametric estimator for the mean cumulative function and the parametric models based on the nonhomogeneous Poisson process.
Abstract: In the age of Big Data, one pressing challenge facing engineers is to perform reliability analysis for a large fleet of heterogeneous repairable systems with covariates. In addition to static covar...
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...
GfK1
TL;DR: The monograph belongs to the series “Use R!” and presents a compendium of classical and modern statistical techniques used in psychometrics, with their implementations in R packages demonstrated by researchers.
Abstract: The monograph belongs to the series “Use R!” and presents a compendium of classical and modern statistical techniques used in psychometrics, with their implementations in R packages demonstrated by...
TL;DR: A class of statistical tests for trend in time censored recurrent event data, based on the null hypothesis of a renewal process, is presented, constructed by an adaption of a functional central limit theorem for renewal processes.
Abstract: Statistical tests for trend in recurrent event data not following a Poisson process are generally constructed for event censored data. However, time censored data are more frequently encoun...
TL;DR: The proposed additive heredity model (AHM) considers an additive structure to inherently connect the major components with the minor components in mixture-of-mixtures experiments.
Abstract: The mixture-of-mixtures (MoM) experiment is different from the classical mixture experiment in that the mixture component in MoM experiments, known as the major component, is made up of subcomponen...
TL;DR: Ridge regression and the idea of regularization that it comes to symbolize are ubiquitous in modern data analysis since its formal introduction to statistics about half a century ago by Hoerl and K...
Abstract: Ridge regression and the idea of regularization that it comes to symbolize are ubiquitous in modern data analysis since its formal introduction to statistics about half a century ago by Hoerl and K...
TL;DR: It is demonstrated that in the presence of error-in-covariates, even when using a Lasso-variant that adjusts for measurement error, application of naive leave-one-out cross-validation to select the tuning parameter can be problematic.
Abstract: Variants of the Lasso or l1 -penalized regression have been proposed to accommodate for presence of measurement errors in the covariates. Theoretical guarantees of these estimates have been...
TL;DR: A Bayesian nonparametric joint mixture model for clustering spatially correlated time series based on both spatial and temporal similarities is developed, which model the spatial distribution of each cluster as a DP Gaussian mixture density.
Abstract: We develop a Bayesian nonparametric joint mixture model for clustering spatially correlated time series based on both spatial and temporal similarities. In the temporal perspective, the pattern of ...
TL;DR: A robust sparsePCA method is proposed to handle potential outliers in the data based on the least trimmed squares PCA method which provides robust but non-sparse PC estimates and the computation time is reduced to a great extent.
Abstract: Sparse principal component analysis (PCA) is used to obtain stable and interpretable principal components (PCs) from high-dimensional data. A robust sparse PCA method is proposed to handle potentia...
TL;DR: The first edition of this book was released in 2013 and like the first edition the aim and scope of this edition were the same.
Abstract: The first edition of this book was released in 2013. I do not find any record of a previously reviewed edition in the Technometrics Journal. Like the first edition the aim and scope of this edition...
TL;DR: Ridge regression was originally introduced by Hoerl and Kennard (1970) to deal with collinearity issue in linear regression in the presence of highly correlated covariates and solves l2 penalized l...
Abstract: Ridge regression was originally introduced by Hoerl and Kennard (1970) to deal with collinearity issue in linear regression in the presence of highly correlated covariates. It solves l2 penalized l...
GfK1
TL;DR: In this paper, the Springer series on Quantitative Methods in the Humanities and Social Sciences (QUMS) is presented, which consists of six chapters presenting papers by known experts in the language-related an...
Abstract: This book belongs to the Springer series on Quantitative Methods in the Humanities and Social Sciences, and it consists of six chapters presenting papers by known experts in the language-related an...
TL;DR: Ahmed as mentioned in this paper reviewed those books whose content and level reflect the general editorial policy of Technometrics, and sent books for review to Ejaz Ahmed, Department of Mathematics and Science.
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 ...
TL;DR: In this article, the authors focus on the parametric linear regression problem in high-dimensional settings, which has recently attracted enormous attention within the literature, and most published work focuses on parametric LRL.
Abstract: Statistical inference in high-dimensional settings has recently attracted enormous attention within the literature. However, most published work focuses on the parametric linear regression problem....