On the Podium…

On the Podium… It's the Olympics, and I, like a lot of other people, am losing a lot of sleep watching the television coverage from the winter events in South Korea. I find myself particularly attracted to the Alpine events and hockey. My university, Arizona State University, has only recently fielded a surprisingly competitive Division I NCAA men's hockey team, and this likely has rekindled my interests in the sport.The Olympic season has inspired me to examine the recently completed 2017 Volume 33 of this journal to try to identify my choices as the “medalists” from among the numerous excellent papers that have appeared. Now a note of warning. I have not used any objective judging criteria: citations, subject matter, no collaboration with my co‐chief editors, no advice from a panel of experts. Nothing but my own probably highly biased opinion. I am not going to award gold, silver, or bronze medal rankings either. I am just going to list the papers in reverse order of publication date (issue number), offer a few brief comments about each paper, and let you, the reader, award the medal positions. Or you can choose to completely disagree with my choices, in which case please email me and offer your selections as an alternative. They might appear in a future editorial.The most recently published paper is by Janj and Anderson‐Cook (). Their paper studies the effectiveness of 2 popular techniques for analyzing the data from unreplicated factorial designs, the normal (or equivalently the half‐normal) probability plot, and a least absolute shrinkage and selection operation plot. They investigate how these methods perform when either 1 observation is missing or when there is an outlier in the data. Because these designs are so widely used in practice, the authors' work should be of wide interest to industrial statisticians.The next paper is Capaci, Bergquist, Kulahci, and Vanhatalo (), which considers using designed experiments in processes that are operating under closed‐loop engineering process control. There is a well‐established literature on the integration of statistical process monitoring with engineering process control, but little work has been reported on using designed experiments in these situations. The authors study 2 situations. One explores the impact of experimental factors that may be considered as disturbances in a closed‐loop system. The second studies an experiment using the set‐points of the controllers as the design factors. Examples of how to analyze the 2 situations are presented.The last paper is Steward and Rigdon (). Risk adjustment is widely used in monitoring health care outcomes. These procedures take into account measures of the patient condition and how these measures are related to the outcomes. When the outcome is dichotomous, such as survival/death, the modeling involves logistic regression to assess the relationship between the predictor(s) and the outcome. Most risk‐adjusted control charts are designed to detect a change in the log‐odds of the adverse outcome. These authors address risk‐adjusted monitoring as a change‐point problem with several possible change‐point models. Their approach generalizes previous risk‐adjusted charts in that they try to detect changes in any of the underlying model parameters. They adopt a Bayesian approach and find the posterior distribution for the model (ie, which coefficients changed), the time of the change, and the values of the parameters for those that changed. All 3 tasks are accomplished in the context of a single model. They apply reversible jump MCMC to account for the variable size of the parameter space.All 3 of these papers are typical of the types of work that this journal hopes to attract. They address important practical challenges, they adopt modern methods to address the problems, and they are clearly written so that the work is accessible to the widest possible audiences.REFERENCESJanj D‐H, Anderson‐Cook CM. Examining robustness of model selection with half‐normal and LASSO plots for unreplicated factorial designs. Quality and Reliability Engineering International. 2017;33(8):1921‐1928.Capaci F, Bergquist B, Kulahci M, Vanhatalo E. Exploring the use of design of experiments in industrial processes operating under closed‐loop control. Quality and Reliability Engineering International. 2017;33(7):1601‐1614.Steward RM, Rigdon SE. Risk‐adjusted monitoring of healthcare quality: model selection and change‐point estimation. 2017;33(5):979‐992. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality and Reliability Engineering International Wiley

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Publisher
Wiley
Copyright
Copyright © 2018 John Wiley & Sons, Ltd.
ISSN
0748-8017
eISSN
1099-1638
D.O.I.
10.1002/qre.2296
Publisher site
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Abstract

It's the Olympics, and I, like a lot of other people, am losing a lot of sleep watching the television coverage from the winter events in South Korea. I find myself particularly attracted to the Alpine events and hockey. My university, Arizona State University, has only recently fielded a surprisingly competitive Division I NCAA men's hockey team, and this likely has rekindled my interests in the sport.The Olympic season has inspired me to examine the recently completed 2017 Volume 33 of this journal to try to identify my choices as the “medalists” from among the numerous excellent papers that have appeared. Now a note of warning. I have not used any objective judging criteria: citations, subject matter, no collaboration with my co‐chief editors, no advice from a panel of experts. Nothing but my own probably highly biased opinion. I am not going to award gold, silver, or bronze medal rankings either. I am just going to list the papers in reverse order of publication date (issue number), offer a few brief comments about each paper, and let you, the reader, award the medal positions. Or you can choose to completely disagree with my choices, in which case please email me and offer your selections as an alternative. They might appear in a future editorial.The most recently published paper is by Janj and Anderson‐Cook (). Their paper studies the effectiveness of 2 popular techniques for analyzing the data from unreplicated factorial designs, the normal (or equivalently the half‐normal) probability plot, and a least absolute shrinkage and selection operation plot. They investigate how these methods perform when either 1 observation is missing or when there is an outlier in the data. Because these designs are so widely used in practice, the authors' work should be of wide interest to industrial statisticians.The next paper is Capaci, Bergquist, Kulahci, and Vanhatalo (), which considers using designed experiments in processes that are operating under closed‐loop engineering process control. There is a well‐established literature on the integration of statistical process monitoring with engineering process control, but little work has been reported on using designed experiments in these situations. The authors study 2 situations. One explores the impact of experimental factors that may be considered as disturbances in a closed‐loop system. The second studies an experiment using the set‐points of the controllers as the design factors. Examples of how to analyze the 2 situations are presented.The last paper is Steward and Rigdon (). Risk adjustment is widely used in monitoring health care outcomes. These procedures take into account measures of the patient condition and how these measures are related to the outcomes. When the outcome is dichotomous, such as survival/death, the modeling involves logistic regression to assess the relationship between the predictor(s) and the outcome. Most risk‐adjusted control charts are designed to detect a change in the log‐odds of the adverse outcome. These authors address risk‐adjusted monitoring as a change‐point problem with several possible change‐point models. Their approach generalizes previous risk‐adjusted charts in that they try to detect changes in any of the underlying model parameters. They adopt a Bayesian approach and find the posterior distribution for the model (ie, which coefficients changed), the time of the change, and the values of the parameters for those that changed. All 3 tasks are accomplished in the context of a single model. They apply reversible jump MCMC to account for the variable size of the parameter space.All 3 of these papers are typical of the types of work that this journal hopes to attract. They address important practical challenges, they adopt modern methods to address the problems, and they are clearly written so that the work is accessible to the widest possible audiences.REFERENCESJanj D‐H, Anderson‐Cook CM. Examining robustness of model selection with half‐normal and LASSO plots for unreplicated factorial designs. Quality and Reliability Engineering International. 2017;33(8):1921‐1928.Capaci F, Bergquist B, Kulahci M, Vanhatalo E. Exploring the use of design of experiments in industrial processes operating under closed‐loop control. Quality and Reliability Engineering International. 2017;33(7):1601‐1614.Steward RM, Rigdon SE. Risk‐adjusted monitoring of healthcare quality: model selection and change‐point estimation. 2017;33(5):979‐992.

Journal

Quality and Reliability Engineering InternationalWiley

Published: Jan 1, 2018

References

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