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.

The modern development of data acquisition technologies in many industrial processes is facilitating the collection of quality characteristics that are apt to be modelled as functions, which are usually referred to as profiles. At the same time, measurements of concurrent variables, which are related to the quality characteristic profiles, are often available in a functional form as well, and usually

(2020). Detection of latent heteroscedasticity and group-based regression effects in linear models via Bayesian model selection. Technometrics. Ahead of Print.

This paper 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 implementation

In the pricing of customized products, it is challenging to accurately predict the purchase likelihood of potential clients for each personalized request. The heterogeneity of customers and their responses to the personalized products leads to very different purchase behavior. Thus, it is often not appropriate to use a single model to analyze all the pricing data. There is a great need to construct

In the fabrication of artificial soft tissues, novel biomaterials with the required properties are obtained by appropriately adjusting process parameters during material synthesis. One key step in finding the desired material is understanding the relationship between the process parameters and the material properties, and time-course experiments are typically conducted for this purpose. This article

(2020). Sequential Optimization in Locally Important Dimensions. Technometrics. Ahead of Print.

Online experiments and specifically A/B testing are commonly used to identify whether a proposed change to a web page is in fact an effective one. This study focuses on basic settings in which a binary outcome is obtained from each user who visits the website and the probability of a response may be affected by numerous factors. We use Bayesian probit regression to model the factor effects and combine

Traffic demand prediction has been a crucial problem for the planning, scheduling, and optimization in transportation management. The prediction of traffic demand counts for origin-destination (OD) pairs has been considered challenging due to the high variability and complicated spatiotemporal correlations in the data. Though several articles have considered estimating traffic flows from counts observed

Model calibration uses outputs from a simulator and field data to build a predictive model for the physical system and to estimate unknown inputs. The conventional approach to model calibration assumes that the observations are continuous outcomes. In many applications this is not the case. The methodology proposed was motivated by an application in modeling photon counts at the Center for Exascale

In recent years, measurement or collection of heterogeneous sets of data such as those containing scalars, waveform signals, images, and even structured point clouds, has become more common. Statistical models based on such heterogeneous sets of data that represent the behavior of an underlying system can be used in the monitoring, control, and optimization of the system. Unfortunately, available methods

We propose a flexible approach to modeling for renewal processes. The model is built from a structured mixture of Erlang densities for the renewal process inter-arrival density. The Erlang mixture components have a common scale parameter, and the mixture weights are defined through an underlying distribution function modeled nonparametrically with a Dirichlet process (DP) prior. This model specification

Accelerated repeated measures degradation tests are often used to assess product or component reliability when there would be few or even no failures during a traditional life test. Such tests are used to estimate the failure-time distributions of highly reliable items in applications where it is possible to take repeated measures of some appropriate degradation measure. When engineers have valid prior

(2019). Adaptive Design of Experiments for Conservative Estimation of Excursion Sets. Technometrics. Ahead of Print.

Many methods for modeling functions over high-dimensional spaces assume global smoothness properties; such assumptions are often violated in practice. We introduce a method for modeling functions that display heterogeneity or contain discontinuities. The heterogeneity is dealt with by using a combination of Voronoi tessellation, to partition the input space, and separate Gaussian processes to model

Spatial boundary analysis has attained considerable attention in several disciplines including engineering, shape analysis, spatial statistics, and computer science. The inferential question of interest is often to identify rapid surface change of an unobserved latent process. Curvilinear wombling and crisp wombling (or fuzzy) are two major approaches that have emerged in Bayesian spatial statistics

Motivated by the problem of detecting changes in two-dimensional X-ray diffraction data, we propose a Bayesian spatial model for sparse signal detection in image data. Our model places considerable mass near zero and has heavy tails to reflect the prior belief that the image signal is zero for most pixels and large for an important subset. We show that the spatial prior places mass on nearby locations

Abstract The product moment covariance matrix is a cornerstone of multivariate data analysis, from which one can derive correlations, principal components, Mahalanobis distances and many other results. Unfortunately, the product moment covariance and the corresponding Pearson correlation are very susceptible to outliers (anomalies) in the data. Several robust estimators of covariance matrices have

Penalized regression methods that perform simultaneous model selection and estimation are ubiquitous in statistical modeling. The use of such methods is often unavoidable as manual inspection of all possible models quickly becomes intractable when there are more than a handful of predictors. However, automated methods usually fail to incorporate domain-knowledge, exploratory analyses, or other factors

Classification problems are commonly seen in practice. In this paper, we aim to develop classifiers that can enjoy great interpretability as linear classifiers, and at the same time have model flexibility as nonlinear classifiers. We propose convex bidirectional large margin classifiers to fill the gap between linear and general nonlinear classifiers for high dimensional data. Our method provides a

Sonar emits pulses of sound and uses the reflected echoes to gain information about target objects. It offers a low cost, complementary sensing modality for small robotic platforms. While existing analytical approaches often assume independence across echoes, real sonar data can have more complicated structures due to device setup or experimental design. In this paper, we consider sonar echo data collected