Assessing robustness of factor ranking for supersaturated designs

Assessing robustness of factor ranking for supersaturated designs Supersaturated designs can potentially be a beneficial tool for efficiently exploring a large number of factors with a moderately sized design. However, because more factors are being considered than there are runs, the stability of the identified factors depends heavily on effect sparsity and the lack of highly influential observations. A helpful tool for the analysis of supersaturated designs is least absolute shrinkage and selection operation (LASSO), which is useful when the effects of many explanatory variables are sparse in a high‐dimensional dataset. To understand the impact of individual observations on the selected factors, the LASSO influence plot was created. This paper describes an application of this plot and its variants that can be used to identify influential points, increase understanding of the impact of individual observations on model parameters, and the robustness of results in analyses with supersaturated designs. These graphical methods can serve as a complement to other regression diagnostics techniques in the LASSO regression setting. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality and Reliability Engineering International Wiley

Assessing robustness of factor ranking for supersaturated designs

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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.2262
Publisher site
See Article on Publisher Site

Abstract

Supersaturated designs can potentially be a beneficial tool for efficiently exploring a large number of factors with a moderately sized design. However, because more factors are being considered than there are runs, the stability of the identified factors depends heavily on effect sparsity and the lack of highly influential observations. A helpful tool for the analysis of supersaturated designs is least absolute shrinkage and selection operation (LASSO), which is useful when the effects of many explanatory variables are sparse in a high‐dimensional dataset. To understand the impact of individual observations on the selected factors, the LASSO influence plot was created. This paper describes an application of this plot and its variants that can be used to identify influential points, increase understanding of the impact of individual observations on model parameters, and the robustness of results in analyses with supersaturated designs. These graphical methods can serve as a complement to other regression diagnostics techniques in the LASSO regression setting.

Journal

Quality and Reliability Engineering InternationalWiley

Published: Jan 1, 2018

Keywords: ; ; ; ;

References

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