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 review the rapidly growing literature on auxiliary information-based (AIB) process monitoring methods. Under this approach, there is an assumption that the auxiliary variable, which is correlated with the quality variable of interest, has a known mean, or some other parameter, which cannot change over time. We demonstrate that violations of this assumption can have serious adverse effects both when the process is stable and when there has been a process shift. Some process shifts can become undetectable. We also show that the basic AIB approach is a special case of simple linear regression profile monitoring. The AIB charting techniques require strong assumptions. Based on our results, we warn against the use of AIB approach in quality control applications.

A Review and Critique of Auxiliary Information-Based Process Monitoring Methods.

In the last decade numerous papers have appeared in the statistical process monitoring literature where the in‐control and out‐of‐control average run length (ARL) profiles of the exponentially weighted moving average chart, and some of its variants, have been approximated under the assumption that the signal event can be represented by a sequence of independent Bernoulli random variables. In this short note we show that these approximations, presented in the literature as being exact, can be very inaccurate when compared with the true ARL values. Moreover, the use of these poor ARL approximations has led to many flawed articles, which should be either corrected or retracted.

A note on an average run length calculation for the EWMA and other charts

A Shewhart procedure is used to simultaneously control the standard deviations of quality characteristics assumed to have a bivariate normal distribution. Following Krishnaiah et al (1963), we use the bivariate chi-square distribution to determine probabilities of out-of-control signals and thus the respective average run lengths (ARLs). Results from an example indicate that for both one-sided and two-sided cases, signals occur only slightly more quickly for changes in the process standard deviations for uncorrected variables than for correlated variables.

Bivariate dispersion quality control charts

[Shewhart-Type Charts in Nonstandard Situations]: Discussion

Abstract Multiple dependent state sampling (MDSS) is a feature that was recently proposed for acceptance sampling plans. In addition, it is increasingly recommended to use this technique, and its generalization, with quality control charts. In our paper, we show that the MDSS feature is an extension of chain sampling in acceptance sampling applications. In addition, we show that use of the MDSS feature with control charts is equivalent to the use of standard supplementary runs rules. Analytical methods based on Markov chains for determining the performance of control charts with runs rules can also be applied to MDSS acceptance sampling applications. Additionally, we provide a derivation approach for some of the formulas for the performance measures for the MDSS charts and point out some incorrect derivations.

William H. Woodall

Papers

A Review and Critique of Auxiliary Information-Based Process Monitoring Methods.

A note on an average run length calculation for the EWMA and other charts

Bivariate dispersion quality control charts

[Shewhart-Type Charts in Nonstandard Situations]: Discussion

Equivalences between multiple dependent state sampling, chain sampling, and control chart runs rules