Applied Stochastic Models in Business and Industry

Abraham, Bovas; Steinberg, David

doi: 10.1002/asmb.2012pmid: N/A

Hicks, Ronald; Pelham Box, G.E.

doi: 10.1002/asmb.1995pmid: N/A

Stuart Hunter, J.

doi: 10.1002/asmb.2018pmid: N/A

This short essay covers the early professional life of George E. P. Box starting before WWII, followed by his 1953 visit to the Institute of Statistics in Raleigh, NC, and ending with his departure from Princeton University in 1959 to establish the Department of Statistics at Madison, WI. This brief period provides an early glimpse of the vigor and genius of one of the world's great statisticians. Copyright © 2014 John Wiley & Sons, Ltd.

Hill, William J.

doi: 10.1002/asmb.2024pmid: N/A

This article discusses George Box's impact on industry and his desire for his students to listen to industry needs for applied statistics problem‐solving opportunities. Three examples are given including lesser known but nonetheless important ones on how Box's research and outreach impacted industry and attracted industrial support. These include (i) the role of his time series research with Gwilym Jenkins and George Tiao on better understanding environmental problems of air pollution, stratospheric ozone depletion and climate change; (ii) the enabling infrastructure that he set up at the University of Wisconsin including the Department of Statistics and, with Bill Hunter, the Center for Quality and Productivity Improvement; and (iii) his supervision of students, many of whom went into industry positions or interfaced closely with industry from their academic positions on deploying applied statistics in problem‐solving. In his own words in his recent autobiography ‘An Accidental Statistician’, he said ‘I wanted them (meaning his students) to take their ideas out of the classroom, to discuss and to argue them, and to meet industrial statisticians who could explain how they solved problems’. He had great respect for industry professionals and their role in helping him and others in the development of new applied statistical methods for problem‐solving. Copyright © 2014 John Wiley & Sons, Ltd.

Bacon, David W.

doi: 10.1002/asmb.2013pmid: N/A

Discovery generally means becoming aware of something previously unknown. It would surprise me greatly if anyone who encountered George Box, either for the first time or on a later occasion, did not experience a genuine sense of discovery. He was one of the most creative and insightful people I have ever met, and thanks to him, my career during the 52 years since our first meeting has been a wonderful journey of personal discovery. Copyright © 2014 John Wiley & Sons, Ltd.

Ljung, G. M.; Ledolter, J.; Abraham, B.

doi: 10.1002/asmb.2016pmid: N/A

George Edward Pelham Box was born on October 19, 1919 in Gravesend, Kent, UK and died on March 28, 2013 in Madison, Wisconsin, USA. George Box made significant contributions to many fields of statistics including design of experiments and response surface methodology, evolutionary operation, statistical inference, robustness, Bayesian methods, time series analysis and forecasting, and quality improvement. Our paper discusses his contributions to time series analysis and forecasting. His work in this area started in collaboration with Gwilym Jenkins in the early 1960s and continued over the next several decades. His contributions include the classic and extraordinarily influential book ‘Time Series Analysis: Forecasting and Control’ written with Gwilym Jenkins and first published by Holden Day in 1970. Subsequent contributions to time series analysis include joint work with George Tiao, Gregory Reinsel, Daniel Pena, and many former graduate students. His work provided a unified framework for carrying out time series analysis in practice and laid the foundation for many new developments in the field. Copyright © 2014 John Wiley & Sons, Ltd.

Steinberg, David M.

doi: 10.1002/asmb.2017pmid: N/A

George Box was fascinated with how we make discoveries. His path‐breaking contributions to experimental design made statistics an active partner in the process of discovery. Box introduced us to response surface methods, evolutionary operation, resolution and rotatability, projective properties and design robustness. He developed popular experimental plans like the central composite and Box‐Behnken designs. He explored the consequences of imperfect models and derived D‐optimal designs for experiments to estimate mechanistic models. Box's ideas grew from close collaborations with scientists and engineers and have been applied successfully in a wide range of disciplines. He has left an indelible stamp on the field of experimental design and on the practice of scientific investigation. Copyright © 2014 John Wiley & Sons, Ltd.

Jones, Stephen P.

doi: 10.1002/asmb.2023pmid: N/A

George Box's pioneering research in experimental design has impacted many scientific and engineering disciplines. The research was often motivated by the real‐world problems of scientists and engineers. His research in robust design was motivated by engineering applications identified by Genichi Taguchi, that of designing a product or process that is insensitive to environmental conditions and to variation from components.

Woodall, William H.; Castillo, Enrique

doi: 10.1002/asmb.2019pmid: N/A

The purpose of this paper is to summarize the contributions of George Box in the area of process monitoring and control. We give a historical context and then describe his ideas and methods. Copyright © 2014 John Wiley & Sons, Ltd.

Meyer, R. Daniel

doi: 10.1002/asmb.2014pmid: N/A

The Bayesian paradigm was fundamental to George Box's philosophy of statistics. Box's scholarship in statistics was driven by his engagement with other scientists in the process of scientific discovery. In his view, scientific discovery was represented elegantly by Bayes' theorem, in which information from the latest experiment is combined with current knowledge. Applications to real problems was the focus of his research in Bayesian methods, especially problems that were less accessible to classical methods based on sampling theory. These problems typically related to the design of experiments and analysis of experimental data, hierarchical models, the sensitivity of inferences to assumptions about the data, and the use of non‐informative priors. His work with a network of collaborators laid the groundwork for widespread application of Bayesian methods facilitated by later advances in computational methods. Copyright © 2014 John Wiley & Sons, Ltd.