Abstract We develop a real-time anomaly detection method for directed activity on large, sparse networks. We model the propensity for future activity using a dynamic logistic model with interaction terms for sender- and receiver-specific latent factors in addition to sender- and receiver-specific popularity scores; deviations from this underlying model constitute potential anomalies. Latent nodal attributes

Abstract Bayesian computation of high dimensional linear regression models using Markov Chain Monte Carlo (MCMC) or its variants can be extremely slow or completely prohibitive since these methods perform costly computations at each iteration of the sampling chain. Furthermore, this computational cost cannot usually be efficiently divided across a parallel architecture. These problems are aggravated

Abstract Hierarchical spatial models are very flexible and popular for a vast array of applications in areas such as ecology, social science, public health, and atmospheric science. It is common to carry out Bayesian inference for these models via Markov chain Monte Carlo (MCMC). Each iteration of the MCMC algorithm is computationally expensive due to costly matrix operations. In addition, the MCMC

Abstract This article deals with the sequential design of experiments for (deterministic or stochastic) multi-fidelity numerical simulators, that is, simulators that offer control over the accuracy of simulation of the physical phenomenon or system under study. Accurate simulations usually entail a high computational effort, while coarse simulations are obtained at a lower cost. The cost can be measured

Abstract Process monitoring using profile data remains an important and challenging problem in various manufacturing industries. Motivated by an application case of motherboard testing processes, we develop a novel modeling and monitoring framework for heterogeneous multivariate profiles. In this framework, a heterogeneous graphical model is constructed to depict the complicated heterogeneous relationship

Abstract Machine learning methods have been widely used in different applications, including process control and monitoring. For handling statistical process control (SPC) problems, conventional supervised machine learning methods (e.g., artificial neural networks and support vector machines) would have some difficulties. For instance, a training dataset containing both in-control and out-of-control

Abstract Classification is a major tool of statistics and machine learning. A classification method first processes a training set of objects with given classes (labels), with the goal of afterward assigning new objects to one of these classes. When running the resulting prediction method on the training data or on test data, it can happen that an object is predicted to lie in a class that differs

Abstract Motivated by the wind turbine anomaly detection, we propose a Bayesian hierarchical model (BHM) for the mean-change detection in multivariate sequences. By combining the exchange random order distribution induced from the Poisson–Dirichlet process and nonlocal priors, BHM exhibits satisfactory performance for mean-shift detection with multivariate sequences under different error distributions

(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

Manufacturers are required to demonstrate products meet reliability targets. A typical way to achieve this is with reliability demonstration tests (RDTs), in which a number of products are put on test and the test is passed if a target reliability is achieved. There are various methods for determining the sample size for RDTs, typically based on the power of a hypothesis test following the RDT or risk

(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).

We propose a multi-fidelity Bayesian emulator for the analysis of the Weather Research and Forecasting (WRF) model when the available simulations are not generated based on hierarchically nested experimental design. The proposed procedure, called Augmented Bayesian Treed Co-Kriging, extends the scope of co-kriging in two major ways. We introduce a binary treed partition latent process in the multifidelity

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

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 as determined

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

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 variabilit...

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

(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.

(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.

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 depth metric called elastic depth 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 than the rest of the data. Identifying them is far more difficult than identifying magnitude anomalies because shape anomalies are often not separable from the bulk of the data with

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

It was 50 years ago Arthur Hoerl and Robert Kennard published their breakthrough article on ridge regression in Technometrics. I am not sure at that time if they had realized the enormous impact th...

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

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

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 intr...

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