Name
Abstract | ||
|---|---|---|
Nonregular designs are a preferable alternative to regular resolution IV designs because they avoid confounding two-factor interactions. As a result nonregular designs can estimate and identify a few active two-factor interactions. However, due to the sometimes complex alias structure of nonregular designs, standard screening strategies can fail to identify all active effects. In this paper, we explore a specific no-confounding six-factor 16-run nonregular design with orthogonal main effects. By utilizing our knowledge of the alias structure, we can inform the model selection process. Our aliased informed model selection (AIMS) strategy is a design-specific approach that we compare to three generic model selection methods; stepwise regression, Lasso, and the Dantzig selector. The AIMS approach substantially increases the power to detect active main effects and two-factor interactions versus the aforementioned generic methodologies. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1002/qre.2831 | QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL |
Keywords | DocType | Volume |
alias patterns, model selection, nonregular designs, orthogonal designs, screening experiments | Journal | 37 |
Issue | ISSN | Citations |
7 | 0748-8017 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carly E. Metcalfe | 1 | 0 | 0.34 |
| Bradley Jones | 2 | 58 | 13.68 |
| Douglas C. Montgomery | 3 | 106 | 24.05 |