Title
A mixed integer optimization approach for model selection in screening experiments
Author
Vazquez, A.R.
Schoen, E.D.
Goos, P.
Publication year
2020
Abstract
After completing the experimental runs of a screening design, the responses under study are analyzed by statistical methods to detect the active effects. To increase the chances of correctly identifying these effects, a good analysis method should provide alternative interpretations of the data, reveal the aliasing present in the design, and search only meaningful sets of effects as defined by user-specified restrictions such as effect heredity. This article presents a mixed integer optimization strategy to analyze data from screening designs that possesses all these properties. We illustrate our method by analyzing data from real and synthetic experiments, and using simulations. © 2020 American Society for Quality.
Subject
Best-subset selection
Definitive screening design
Quality control
Dantzig selector
Screening design
Sparsity
Subset selection
Two-factor interaction
Integer programming
To reference this document use:
http://resolver.tudelft.nl/uuid:19f640e9-8bd6-4cf4-91cb-2764952d563d
DOI
https://doi.org/10.1080/00224065.2020.1712275
TNO identifier
955363
ISSN
0022-4065
Source
Journal of Quality Technology, 53 (53), 243-266
Document type
article