Journal webinar with Peter Goos: Optimal design: getting more out of experiments with hard-to-change factors
Professor Peter Goos will present a paper that he authored with Bradley Jones, ‘A candidate-set-free algorithm for generating D-optimal split-plot designs’ first published in 2007 in the Royal Statistical Society's Series C Journal (JRSS Series C Vol 56: 3).
This webinar will be of interest to anyone who runs experiments, or advises people who do. Many experiments include some treatment factors whose levels are harder to change than other factors. Rather than making ad hoc adjustments, a structured approach to designing such experiments, using split-plot designs, is the most informative approach. This webinar will describe optimal design methods for such experiments with application to engineering.
The webinar will be chaired by Steve Gilmour. Maria Lanzerath as discussant will bring an industry perspective.
We introduce a new method for generating optimal split-plot designs. These designs are optimal in the sense that they are efficient for estimating the fixed effects of the statistical model that is appropriate given the split-plot design structure. One advantage of the method is that it does not require the prior specification of a candidate set. This makes the production of split-plot designs computationally feasible in situations where the candidate set is too large to be tractable. The method allows for flexible choice of the sample size and supports inclusion of both continuous and categorical factors. The model can be any linear regression model and may include arbitrary polynomial terms in the continuous factors and interaction terms of any order. We demonstrate the usefulness of this flexibility with a 100-run polypropylene experiment involving 11 factors where we found a design that is substantially more efficient than designs that are produced by using other approaches.
About the author
Peter Goos is a professor at the Faculty of Bio-Science Engineering of the University of Leuven and at the Faculty of Applied Economics of the University of Antwerp, where he teaches various introductory and advanced courses on statistics and probability. His main research area is the statistical design and analysis of experiments. He has published books on 'The Optimal Design of Blocked and Split-Plot Experiments', 'Optimal Experimental Design: A Case-Study Approach', 'Statistics with JMP: Graphs, Descriptive Statistics and Probability' and 'Statistics with JMP: Hypothesis Tests, ANOVA and Regression'.
To date, Peter Goos has received the Shewell Award and the Lloyd S Nelson Award of the American Society for Quality, the Ziegel Award and the Statistics in Chemistry Award from the American Statistical Association, and the Young Statistician Award of the European Network for Business and Industrial Statistics.
About the chair
Steven Gilmour graduated with a BSc in Statistics from Heriot-Watt University and a PhD in Applied Statistics from the University of Reading. He was Lecturer in Applied Statistics at Reading from 1990-99, Reader in, then Professor of, Statistics at Queen Mary, University of London from 2000-10 and Professor of Statistics at the University of Southampton from 2010-2016. He is now Professor of Statistics at King’s College London.
Steven has wide-ranging research interests in the theory, methodology and applications of the design and analysis of experiments, especially factorial and response surface designs.
‘A candidate-set-free algorithm for generating D-optimal split-plot designs’ is now free to access.
Organiser Name Judith Shorten
Organising Group(s) Royal Statistical Society