(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

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.

(2020). Elements of Copula Modeling With R. Technometrics: Vol. 62, No. 3, pp. 416-416.

(2020). Spatial Ecology and Conservation Modeling: Applications With R. Technometrics: Vol. 62, No. 3, pp. 416-417.

(2020). Advanced R (2nd ed.) Technometrics: Vol. 62, No. 3, pp. 417-417.

Degradation data have been broadly used for assessing product and system reliability. Most existing work focuses on modeling and analysis of degradation data with a single characteristic. In some degradation tests, interest lies in measuring multiple characteristics of the product degradation process to understand different aspects of the reliability performance, resulting in degradation data with

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 towards zero, but not exactly zero. For this reason, the ridge regression has long been criticized

The receiver operating characteristic (ROC) curve is widely used to assess discriminative accuracy of two groups based on a continuous score. In a variety of applications, the distributions of such scores across the two groups exhibit a stochastic ordering. Specific examples include calibrated biomarkers in medical diagnostics or the output of matching algorithms in biometric recognition. Incorporating

Motivated by brain connectome datasets acquired using diffusion weighted magnetic resonance imaging (DWI), this article proposes a novel generalized Bayesian linear modeling framework with a symmetric tensor response and scalar predictors. The symmetric tensor coefficients corresponding to the scalar predictors are embedded with two features: low-rankness and group sparsity within the low-rank structure

Many well-known sufficient dimension reduction methods investigate the inverse conditional moments of the predictors given the response. The required linearity condition, the number and arrangement of slices, and the inability to detect symmetric dependence are among several long-standing issues that have negatively impacted on the use of these approaches. Motivated by two recent work dealing with

We present a new method for statistical process control (SPC) of a discrete part manufacturing system based on intrinsic geometrical properties of the parts, estimated from 3-dimensional (3D) sensor data. An intrinsic method has the computational advantage of avoiding the difficult part registration problem, necessary in previous SPC approaches of 3D geometrical data, but inadequate if non-contact

Statistical process control (SPC) charts provide a powerful tool for monitoring production lines in manufacturing industries. They are also used widely in other applications, such as sequential monitoring of internet traffic flows, disease incidences, health care systems, and more. In practice, quality/performance variables are often affected in a complex way by many covariates, such as material, labor

The global radiosonde archives contain valuable weather data, such as temperature, humidity, wind speed, wind direction, and atmospheric pressure. Being the only direct measurement of these variables in the upper air, they are prone to errors. Therefore, a robust analysis and outlier detection of radiosonde data is essential. Among all the variables, the radiosonde winds, which consist of wind speed

An order-of-addition experiment is a kind of experiment in which the response is affected by the addition order of materials or components. In many situations, performing the full design with all possible permutations of components is unaffordable, especially when the number of components is larger than four. We introduce a component-position model for analyzing data from such experiments and study

Abstract–This article describes a novel property of likelihood ratio (LR) confidence intervals which is subsequently used to formulate an alternative approach for their calculation. It is shown that LR confidence limits can be defined as the minimum and maximum values of a parameter (or a function of parameters) that satisfy a set value of the log-likelihood. The proposed formulation allows straightforward

(2020). Probability (2nd ed.) Technometrics: Vol. 62, No. 2, pp. 1-290.

(2020). Diagnostic Meta-Analysis: A Useful Tool for Clinical Decision-Making. Technometrics: Vol. 62, No. 2, pp. 1-289.

(2020). Time Series: A First Course With Bootstrap Starter. Technometrics: Vol. 62, No. 2, pp. 1-289.

(2020). Statistical Programing in SAS (2nd ed.) Technometrics: Vol. 62, No. 2, pp. 1-288.

(2020). Statistical Methods in Social Science Research. Technometrics: Vol. 62, No. 2, pp. 1-288.

(2020). A Parametric Approach to a Nonparametric Statistics. Technometrics: Vol. 62, No. 2, pp. 1-287.

(2020). Practical Text Analytics: Maximizing the Value of Text Data. Technometrics: Vol. 62, No. 2, pp. 1-286.

(2020). Statistical Intervals: A Guide for Practitioners and Researchers (2nd ed.) Technometrics: Vol. 62, No. 2, pp. 1-285.