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 recently. These newer developments have led to renewed interest in the original 1970 articles. What has perhaps been lost since 1970 is the context of these classic articles. That is, who were Art Hoerl and Bob Kennard, and what led two statisticians working in the private sector to develop Ridge Regression in the first place? What are the origins of Ridge Regression? Where did the name come from? The purpose of this article is to provide this historical context by discussing the men involved, their work at DuPont, and their approach to methodological development. As Art Hoerl was my father, this is admittedly a personal viewpoint.
The first two phases of the statistical engineering process are to identify the problem, and to properly structure it. These steps relate to work that is often referred to elsewhere as framing of the ...problem. While these are obviously critical steps, we have found that problem-solving teams often "underwhelm" these phases, perhaps being over-anxious to get to the analytics. This approach typically leads to projects that are "dead on arrival" because different parties have different understandings of what problem they are actually trying to solve. In this expository article, we point out evidence for a consistent and perplexing lack of emphasis on these first two phases in practice, review some highlights of previous research on the problem, offer tangible advice for teams on how to properly frame problems to maximize the probability for success, and share some real examples of framing challenging problems.
The study of mixture component effects in the presence of process variables has been of interest since the work of Scheffé. A key advantage of designed experiments in general is the ability to ...estimate and interpret interactions. A unique feature of mixture-process experiments is the potential presence of interactions between the mixture components and the process variables. The classic approach to interpret these has been to use contour plots and evaluate individual interaction coefficients in Scheffé mixture-process models. It is proposed to study the interactions along the Cox component axes, which greatly enhances the insight into the nature of these interactions that can be obtained from contour plots. Further, we propose an alternative analysis that produces estimates of the process variable main effects in mixture-process models. Both graphical and analytical methods are presented. This approach provides an overall view of the main effects and interactions that is consistent with how these terms are evaluated in factorial and response surface experiments with only process variables. Limitations of the classic approach are identified and discussed. Three examples are included to illustrate the approach.
PurposeThis paper aims to present and summarise the arguments for and against the ISO 18404 standard and the perceived advantages and disadvantages of implementing it.Design/methodology/approachA ...qualitative interview approach was utilised by interviewing a panel of leading academics and practitioners familiar with Lean Six Sigma.FindingsThe results indicate that Lean Six Sigma professionals have conflicting opinions on ISO 18404. An overwhelming majority of the panel questioned the “quality” of the standard and whether it is “fit for purpose”, while others see the advantages of a common standard in helping continuous improvement deployment.Research limitations/implicationsAs the standard has not been widely adopted, there were limited examples on ISO 18404 discussion in the literature. Much of the current literature focuses on the theoretical application of the standard, with sparse practical examples providing case study deployment. Also, the interviews were short and at a high level. There is an opportunity for further study and analysis. It was difficult to find qualified interviewees who were familiar with the standard. A very real constraint when conducting research into ISO 18404 is to obtain a balanced view of the standard from those who have a vested interest in its continuation and evolution, or not.Originality/valueThe paper provides a resource for people to obtain insight into the value or non-value add of a standard in Lean Six Sigma and the appropriate details of such a standard. These results can form the basis of a case for the implementation of the standard for those organisations currently trying to decide whether to implement it or not.
Several authors, including the American Statistical Association (ASA) guidelines for undergraduate statistics education
(American Statistical Association Undergraduate Guidelines Workgroup)
, have ...noted the challenges facing statisticians when attacking large, complex, and unstructured problems, as opposed to well-defined textbook problems. Clearly, the standard paradigm of selecting the one "correct" statistical method for such problems is not sufficient; a new paradigm is needed. Statistical engineering has been proposed as a discipline that can provide a viable paradigm to attack such problems, used in conjunction with sound statistical science. Of course, to develop as a true discipline, statistical engineering must be clearly defined and articulated. Further, a well-developed underlying theory is needed, one that would prove helpful in addressing such large, complex, and unstructured problems. The purpose of this expository article is to more clearly articulate the current state of statistical engineering, and make a case for why it merits further study by the profession as a means of addressing such problems. We conclude with a "call to action."
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