(2021). Book Reviews. Technometrics: Vol. 63, No. 2, pp. 272-273.

(2021). Advanced Statistics with Applications in R. Technometrics: Vol. 63, No. 2, pp. 273-275.

(2021). Scientific Journeys: A Physicist Explores the Culture, History and Personalities of Science. Technometrics: Vol. 63, No. 2, pp. 275-277.

(2021). R for Political Data Science: A Practical Guide. Technometrics: Vol. 63, No. 2, pp. 277-278.

(2021). Understanding the Analytic Hierarchy Process. Technometrics: Vol. 63, No. 2, pp. 278-279.

(2021). Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modelling and Analysis of Big Data. Technometrics: Vol. 63, No. 2, pp. 280-280.

Abstract In this article we propose an optimal method referred to as SPlit for splitting a dataset into training and testing sets. SPlit is based on the method of Support Points (SP), which was initially developed for finding the optimal representative points of a continuous distribution. We adapt SP for subsampling from a dataset using a sequential nearest neighbor algorithm. We also extend SP to

Abstract This article introduces methods for constructing prediction bounds or intervals for the number of future failures from heterogeneous reliability field data. We focus on within-sample prediction where early data from a failure-time process is used to predict future failures from the same process. Early data from high-reliability products, however, often have limited information due to some

Abstract Time series modeling has entered an era of unprecedented growth in the size and complexity of data which require new modeling approaches. While many new general purpose machine learning approaches have emerged (Bayer, 2015; Graves, 2013; Pascanu et al., 2012), they remain poorly understand and irreconcilable with more traditional statistical modeling approaches (Box and Jenkins, 1976; Hamilton

Abstract This work proposes a nonparametric method to compare the underlying mean functions given two noisy datasets. The motivation for the work stems from an application of comparing wind turbine power curves. Comparing wind turbine data presents new problems, namely the need to identify the regions of difference in the input space and to quantify the extent of difference that is statistically significant

Super Resolution for Multi-Sources Image Stream Data using Smooth and Sparse Tensor Completion and its Applications in Data Acquisition of Additive Manufacturing. Technometrics. Accepted 13 March 2021.

Abstract Budget constraints become an important consideration in modern predictive modeling due to the high cost of collecting certain predictors. This motivates us to develop cost-constrained predictive modeling methods. In this paper, we study a new high-dimensional cost-constrained linear regression problem, that is, we aim to find the cost-constrained regression model with the smallest expected

Abstract Robustness analysis is an emerging field in the uncertainty quantification domain. It involves analyzing the response of a computer model—which has inputs whose exact values are unknown—to the perturbation of one or several of its input distributions. Practical robustness analysis methods therefore require a coherent methodology for perturbing distributions; we present here one such rigorous

Abstract Scientists use imaging to identify objects of interest and infer properties of these objects. The locations of these objects are often measured with error, which when ignored leads to biased parameter estimates and inflated variance. Current measurement error methods require an estimate or knowledge of the measurement error variance to correct these estimates, which may not be available. Instead

Abstract Observing system uncertainty experiments (OSUEs) have been recently proposed as a cost-effective way to perform probabilistic assessment of retrievals for NASA’s Orbiting Carbon Observatory-2 (OCO-2) mission. One important component in the OCO-2 retrieval algorithm is a full-physics forward model that describes the mathematical relationship between atmospheric variables such as carbon dioxide

Abstract Modern scientific instruments readily record various dynamical phenomena at high frequency and for extended durations. Spanning timescales across several orders of magnitude, such “high-throughput” (HTP) data are routinely analyzed with parametric models in the frequency domain. However, the large size of HTP datasets can render maximum likelihood estimation prohibitively expensive. Moreover

Abstract In modeling spatial processes, a second-order stationarity assumption is often made. However, for spatial data observed on a vast domain, the covariance function often varies over space, leading to a heterogeneous spatial dependence structure, therefore requiring nonstationary modeling. Spatial deformation is one of the main methods for modeling nonstationary processes, assuming the nonstationary

Surface point cloud data from three-dimensional optical scanners provide rich information about the surface geometry of scanned parts and potential variation in the surfaces from part-to-part. It is challenging, however, to make full use of these data for statistical process control purposes to identify sources of variation that manifest in a more complex nonparametric manner than variation in some

Abstract Modern scientific instruments readily record various dynamical phenomena at high frequency and for extended durations. Spanning timescales across several orders of magnitude, such “high-throughput” (HTP) data are routinely analyzed with parametric models in the frequency domain. However, the large size of HTP datasets can render maximum likelihood estimation prohibitively expensive. Moreover

Abstract In modeling spatial processes, a second-order stationarity assumption is often made. However, for spatial data observed on a vast domain, the covariance function often varies over space, leading to a heterogeneous spatial dependence structure, therefore requiring nonstationary modeling. Spatial deformation is one of the main methods for modeling nonstationary processes, assuming the nonstationary

(2021). BOOK REVIEWS. Technometrics: Vol. 63, No. 1, pp. 136-137.

(2021). Debunking Seven Terrorism Myths Using Statistics. Technometrics: Vol. 63, No. 1, pp. 137-140.

(2021). The Equation of Knowledge: From Bayes’ Rule to a Unified Philosophy of Science. Technometrics: Vol. 63, No. 1, pp. 140-143.

(2021). Understanding Elections Through Statistics: Polling, Prediction, and Testing. Technometrics: Vol. 63, No. 1, pp. 143-144.

(2021). Surrogates: Gaussian Process Modeling, Design, and Optimization for the Applied Sciences. Technometrics: Vol. 63, No. 1, pp. 144-145.

(2021). Analytic Methods in Sports: Using Mathematics and Statistics to Understand Data from Baseball, Football, Basketball, and Other Sports, 2nd ed. Technometrics: Vol. 63, No. 1, pp. 145-145.

(2021). Technometrics Editorial Collaborators. Technometrics: Vol. 63, No. 1, pp. (146)-(147).

Abstract We consider binary classification problem in the case where some observations in training data are incorrectly labeled. In presence of such label noise, conventional classification fails to obtain a classifier to be generalized to population. In this work, we investigate label noise logistic regression and explain how it works with noisy training data. We demonstrate that, when label transition

Abstract Estimation of a probability density function (pdf) from its samples, while satisfying certain shape constraints, is an important problem that lacks coverage in the literature. This paper introduces a novel geometric, deformable template constrained density estimator (dtcode) for estimating pdfs constrained to have a given number of modes. Our approach explores the space of thus-constrained

Abstract Manufacturers are required to demonstrate that products meet reliability targets. A way to achieve this is with reliability demonstration tests (RDTs), where a number of products are put on test and the test is passed or failed according to a decision rule based on the observed outcomes. There are various methods for determining the sample size for RDTs, typically based on the power of a hypothesis

Abstract Motivated by a multi-fidelity Weather Research and Forecasting (WRF) climate model application where the available simulations are not generated based on hierarchically nested experimental design, we develop a new co-kriging procedure called Augmented Bayesian Treed Co-Kriging. The proposed procedure extends the scope of co-kriging in two major ways. We introduce a binary treed partition latent

Abstract Computer models with gradient information are increasingly used in engineering and science. The gradient-enhanced Gaussian process emulator can be used for emulating such models. Because the size of the covariance matrix increases proportionally with the dimension of inputs and the sample size, it is computationally challenging to fit such an emulator for large data sets. We propose a random

Abstract Despite the vast amount of literature on supersaturated designs (SSDs), there is a scant record of their use in practice. We contend this imbalance is due to conflicting recommendations regarding SSD use in the literature as well as the designs’ inabilities to meet practitioners’ analysis expectations. To address these issues, we first summarize practitioner concerns and expectations of SSDs

(2020). Editorial: Celebrating 50 Years of Ridge Regression. Technometrics: Vol. 62, No. 4, pp. 419-419.

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 Regression, and of related biased shrinkage estimators, have been extensively developed over the years. Further, newer shrinkage estimators, such as the Lasso and the Elastic Net, have become popular more

(2020). Comment: Feature Screening and Variable Selection via Iterative Ridge Regression. Technometrics: Vol. 62, No. 4, pp. 434-437.

(2020). Comment: Regularization via Bayesian Penalty Mixing. Technometrics: Vol. 62, No. 4, pp. 438-442.

(2020). Comment: Ridge Regression and Regularization of Large Matrices. Technometrics: Vol. 62, No. 4, pp. 443-446.

(2020). Comment: From Ridge Regression to Methods of Regularization. Technometrics: Vol. 62, No. 4, pp. 447-450.

(2020). Comment: Ridge Regression, Ranking Variables and Improved Principal Component Regression. Technometrics: Vol. 62, No. 4, pp. 451-455.

Abstract It is very interesting to learn the history of ridge analysis/ridge regression as well as stories of its inventors from Professor Hoerl’s article. The overview article has covered many important aspects of ridge regression, regularization more generally, and their modern applications. Ridge has indeed become an essential concept in data science. My comments will focus on two new results related

(2020). 2ˆ5 Problems for STEM Education. Technometrics: Vol. 62, No. 4, pp. 557-558.

(2020). Handbook of Military and Defense Operations Research. Technometrics: Vol. 62, No. 4, pp. 559-559.

(2020). Integrals Related to the Error Function. Technometrics: Vol. 62, No. 4, pp. 560-560.

(2020). Machine Learning: A Practical Approach on the Statistical Learning. Technometrics: Vol. 62, No. 4, pp. 560-561.

(2020). Modelling Spatial and Spatial-Temporal Data: A Bayesian Approach. Technometrics: Vol. 62, No. 4, pp. 561-561.

(2020). Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare. Technometrics: Vol. 62, No. 4, pp. 561-562.

Abstract In experiments where observations on each experimental unit are functional in nature, it is often the case that, in addition to variability along the horizontal axis (height or amplitude variability), there are also lateral displacements/deformations in curves (referred to as phase variability). Unlike the former, the latter form of variability is often treated as a nuisance parameter when

Abstract We present a non-parametric prognostic framework for individualized event prediction based on joint modeling of both time series and time-to-event data. Our approach exploits a multivariate Gaussian convolution process (MGCP) to model the evolution of time series signals and a Cox model to map time-to-event data with time series data modeled through the MGCP. Taking advantage of the unique

In many areas of science and engineering, discovering the governing differential equations from the noisy experimental data is an essential challenge. It is also a critical step in understanding the physical phenomena and prediction of the future behaviors of the systems. However, in many cases, it is expensive or time-consuming to collect experimental data. This article provides an active learning

We propose a new family of depth measures called the elastic depths that can be used to greatly improve shape anomaly detection in functional data. Shape anomalies are functions that have considerably different geometric forms or features from the rest of the data. Identifying them is generally more difficult than identifying magnitude anomalies because shape anomalies are often not distinguishable

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. In this brief ridge fest, I have collected together some of the magic and beauty of ridge that my colleagues and I have encountered over the past 40 years in applied statistics.

Two-dimensional layered materials/flakes, also known as crystalline atom-thick layer nanosheets, have recently been receiving great attention in electronics fabrication due to their unique and intriguing properties. The k-layer coverage area (i.e., the area covered by k number of overlapping layers) of the printed flake pattern significantly impacts on the properties of the printed electronics. In

Abstract Ridge regression was introduced to deal with the instability issue of the ordinary least squares estimate due to multicollinearity. It essentially penalizes the least squares loss by applying a ridge penalty on the regression coefficients. The ridge penalty shrinks the regression coefficient estimate toward zero, but not exactly zero. For this reason, the ridge regression has long been criticized

Functional linear regression is a widely used approach to model functional responses with respect to functional inputs. However, classical functional linear regression models can be severely affected by outliers. We therefore introduce a Fisher-consistent robust functional linear regression model that is able to effectively fit data in the presence of outliers. The model is built using robust functional

Multivariate functional data from a complex system are naturally high-dimensional and have a complex cross-correlation structure. The complexity of data structure can be observed as that (1) some functions are strongly correlated with similar features, while some others may have almost no cross-correlations with quite diverse features; and (2) the cross-correlation structure may also change over time

3D-printed medical prototypes, which use synthetic metamaterials to mimic biological tissue, are becoming increasingly important in urgent surgical applications. However, the mimicking of tissue mechanical properties via 3D-printed metamaterial can be difficult and time-consuming, due to the functional nature of both inputs (metamaterial structure) and outputs (mechanical response curve). To deal with

(2020). Computer Intensive Methods in Statistics. Technometrics: Vol. 62, No. 3, pp. 415-415.

(2020). Probability and Bayesian Modeling. Technometrics: Vol. 62, No. 3, pp. 415-416.

(2020). The Measurement of Association: A Permutation Statistical Approach. Technometrics: Vol. 62, No. 3, pp. 416-416.