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

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

Since the seminal work of Hoerl and Kennard (1970), the ridge regression has been formulated as $\ell_2$regularization and extended to solve various challenging problems in statistics. This paper describes the relationships between regularization methods and variable selection in linear models and generalized linear models. It also illustrates how to use the idea of ridge regression for feature screening

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

We view ridge regression through the lens of eigenvalue shrinkage, and consider its influence on two modern problems in high-dimensional statistical inference: covariance estimation and community detection in networks.

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.

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

(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.

(2020). Statistical Inference via Data Science: A Modern Dive Into R and the Tidyverse. Technometrics: Vol. 62, No. 2, pp. 1-283.

(2020). Practical Multivariate Analysis (6th ed.) Technometrics: Vol. 62, No. 2, pp. 1-283.

(2020). Introduction to Data Science: Data Analysis and Prediction Algorithms With R. Technometrics: Vol. 62, No. 2, pp. 1-282.

(2020). Handbook of the Shapley Value. Technometrics: Vol. 62, No. 2, pp. 1-280.

(2020). The Information Theory of Comparisons, With Applications to Statistics and the Social Sciences. Technometrics: Vol. 62, No. 2, pp. 1-278.

(2020). Data Science for Wind Energy. Technometrics: Vol. 62, No. 2, pp. 1-278.

This article introduces an interpolation-based method, called the reconstruction approach, for nonparametric regression. Based on the fact that interpolation usually has negligible errors compared to statistical estimation, the reconstruction approach uses an interpolator to parameterize the regression function with its values at finite knots, and then estimates these values by (regularized) least

(2020). 2018 Ziegel Award Announcement. Technometrics: Vol. 62, No. 1, pp. 145-145.

(2020). The 2018 Technometrics Prizes. Technometrics: Vol. 62, No. 1, pp. 141-144.

(2020). Practical Tools for Designing and Weighting Survey Samples (2nd ed.) Technometrics: Vol. 62, No. 1, pp. 140-140.

(2020). Sequence Analysis and Related Approaches: Innovative Methods and Applications. Technometrics: Vol. 62, No. 1, pp. 139-140.

(2020). Parametric and Nonparametric Statistics for Sample Surveys and Customer Satisfaction Data. Technometrics: Vol. 62, No. 1, pp. 138-139.

(2020). Let the Evidence Speak—Using Bayesian Thinking in Law, Medicine, Ecology and Other Areas. Technometrics: Vol. 62, No. 1, pp. 137-138.

(2020). Lexical Collocation Analysis: Advances and Applications. Technometrics: Vol. 62, No. 1, pp. 137-137.

(2020). Modern Psychometrics With R. Technometrics: Vol. 62, No. 1, pp. 135-137.

(2020). Prehistoric Warfare and Violence: Quantitative and Qualitative Approaches. Technometrics: Vol. 62, No. 1, pp. 132-135.

(2020). Tracing the Life Cycle of Ideas in the Humanities and Social Sciences. Technometrics: Vol. 62, No. 1, pp. 130-132.

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 is generated. In general, fixed effects and random effects in the multi-array Raman data are associated with different quality characteristics such as fabrication consistency, uniformity, and defects. The

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 encountered in practice. In this article, we contribute to filling an important gap in the literature on trend testing by presenting a class of statistical tests for trend in time censored recurrent event data, based on

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 streams responsible has also become increasingly important in many data-rich statistical process control applications. Existing diagnostic procedures, designed for low/moderate dimensional multivariate process

Abstract–Statistical process control (SPC) charts are critically important for quality control and management in manufacturing industries, environmental monitoring, disease surveillance, and many other applications. Conventional SPC charts are designed for cases when process observations are independent at different observation times. In practice, however, serial data correlation almost always exists

Textbooks on response surface methodology generally stress the importance of lack-of-fit tests and estimation of pure error. For lack-of-fit tests to be possible and other inference to be unbiased, experiments should allow for pure-error estimation. Therefore, they should involve replicated treatments. While most textbooks focus on lack-of-fit testing in the context of completely randomized designs

Abstract This paper introduces a new class of Hadamard matrix-based mixed-level foldover designs (MLFODs) and an algorithm which facilitates the construction of MLFODs. Our new MLFODs were constructed by converting some 2-level columns of a Hadamard matrix to 3-level ones. Like the 2-level foldover designs (FODs), each new MLFOD was constructed by a half fraction and its foldover. Our Hadamard-matrix

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 design for m quantitative factors, construct a definitive screening design for more than m factors and drop the superfluous columns. This is done when the number of runs in the standard m-factor definitive screening

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 consider three-level RSDs, which can be viewed as matrices with entries equal to {−1,0,1}. Each column of an RSD corresponds to a factor and each row to an experimental test. We define a new family of orthogonal RSDs

(2020). Multivariate Design of Experiments for Engineering Dimensional Analysis. Technometrics: Vol. 62, No. 1, pp. 6-20.

(2020). Technometrics 2019 Editor's Report. Technometrics: Vol. 62, No. 1, pp. 1-5.