Chapter 11 includes more case studies in other areas, ranging from manufacturing to marketing research. Chapter 12 concludes the book with some commentary about the scienti c contributions of MTS. The Taguchi method for design of experiment has generated considerable controversy in the statistical community over the past few decades. The MTS/MTGS method seems to lead another source of discussions on the methodology it advocates (Montgomery 2003). As pointed out by Woodall et al. (2003), the MTS/MTGS methods are considered ad hoc in the sense that they have not been developed using any underlying statistical theory. Because the “normal” and “abnormal” groups form the basis of the theory, some sampling restrictions are fundamental to the applications. First, it is essential that the “normal” sample be uniform, unbiased, and/or complete so that a reliable measurement scale is obtained. Second, the selection of “abnormal” samples is crucial to the success of dimensionality reduction when OAs are used. For example, if each abnormal item is really unique in the medical example, then it is unclear how the statistical distance MD can be guaranteed to give a consistent diagnosis measure of severity on a continuous scale when the larger-the-better type S/N ratio is used. Multivariate diagnosis is not new to Technometrics readers and is now becoming increasingly more popular in statistical analysis and data mining for knowledge discovery. As a promising alternative that assumes no underlying data model, The Mahalanobis–Taguchi Strategy does not provide suf cient evidence of gains achieved by using the proposed method over existing tools. Readers may be very interested in a detailed comparison with other diagnostic tools, such as logistic regression and tree-based methods. Overall, although the idea of MTS/MTGS is intriguing, this book would be more valuable had it been written in a rigorous fashion as a technical reference. There is some lack of precision even in several mathematical notations. Perhaps a follow-up with additional theoretical justi cation and careful case studies would answer some of the lingering questions.

The Elements of Statistical Learning

According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

Out of the Crisis

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

This book is downloadable from http://www.irisa.fr/sisthem/kniga/. Many monitoring problems can be stated as the problem of detecting a change in the parameters of a static or dynamic stochastic system. The main goal of this book is to describe a unified framework for the design and the performance analysis of the algorithms for solving these change detection problems. Also the book contains the key mathematical background necessary for this purpose. Finally links with the analytical redundancy approach to fault detection in linear systems are established. We call abrupt change any change in the parameters of the system that occurs either instantaneously or at least very fast with respect to the sampling period of the measurements. Abrupt changes by no means refer to changes with large magnitude; on the contrary, in most applications the main problem is to detect small changes. Moreover, in some applications, the early warning of small - and not necessarily fast - changes is of crucial interest in order to avoid the economic or even catastrophic consequences that can result from an accumulation of such small changes. For example, small faults arising in the sensors of a navigation system can result, through the underlying integration, in serious errors in the estimated position of the plane. Another example is the early warning of small deviations from the normal operating conditions of an industrial process. The early detection of slight changes in the state of the process allows to plan in a more adequate manner the periods during which the process should be inspected and possibly repaired, and thus to reduce the exploitation costs.

Detection of abrupt changes: theory and application

We develop a method for monitoring autocorrelated and independent processes based on the number of consecutive values above or below a threshold level. Our runs rule based method is compared to the level crossing control chart proposed by Willemain and Runger (1994). Monte Carlo simulations are used to show that the runs rule based method detects large and small shifts in the process mean more quickly than the level crossing method. The runs rule based method is widely applicable to high output processes, robust to the marginal distribution of the data, highly accessible to practitioners, and effectively detects shifts in the porcess mean.

A runs rule alternative to level crossings in statistical process control

Its wide application in practice makes the monitoring of the rate of rare events a popular research topic. Recently a researcher proposed plotting the counts between events on an individuals X-chart with an upper control limit to detect process improvement and plotting the reciprocals of the counts on an X-chart to detect process deterioration. He also used the median as the center line and the median moving range to obtain control limits in both control charts to address the problem of the standard deviation estimate inflation caused by extreme values. In our paper, we investigated the statistical performance of the four proposed approaches using simulation. We find using the mean results in a high proportion of ineffective control limits, while using the median avoids the issue of ineffective control limits but produces an unacceptably high proportion of false alarms. Copyright © 2016 John Wiley & Sons, Ltd.

An Evaluation of Wheeler's Method for Monitoring the Rate of Rare Events

We appreciate the opportunity to discuss John Sall's interesting article. We agree fully with his comments that when scaling up to thousands of variables one wants to do the following: 1. use summa...

Discussion of “Scaling-up process characterization”

We examine one of the methods implemented by the U.S. Centers for Disease Control and Prevention’s (CDC) BioSense program. The program uses data from hospitals and public health departments to detect outbreaks using the Early Aberration Reporting System (EARS). The EARS W2r method allows one to monitor the proportion of counts of a particular syndrome at a facility relative to the total number of visits. We investigate the performance of the W2r method with negative binomial inputs designed using an empirical recurrence interval (RI). An adaptive threshold monitoring method is studied based on estimating the underlying negative binomial distributions, then converting the current counts to a Z-score through a p-value. We study the effect of the input distributions on the upper thresholds required for both Shewhart and exponentially weighted moving average (EWMA) versions of the W2r and adaptive threshold methods. We simulate 1-week outbreaks and compare the outbreak detection properties of the methods.

Adaptive Threshold Methods for Monitoring Rates in Public Health Surveillance

(2014). Introduction to Statistical Process Control. Journal of Quality Technology: Vol. 46, No. 2, pp. 181-183.

William H. Woodall

Papers

A runs rule alternative to level crossings in statistical process control

An Evaluation of Wheeler's Method for Monitoring the Rate of Rare Events

Discussion of “Scaling-up process characterization”

Adaptive Threshold Methods for Monitoring Rates in Public Health Surveillance

Introduction to Statistical Process Control