A mixed integer optimization approach for model selection in screening experiments

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.

Publication year:2020

Keywords:A1 Journal article

BOF-keylabel:yes

BOF-publication weight:2

CSS-citation score:2

Authors:International

Authors from:Higher Education

Accessibility:Closed