Aliased informed model selection strategies for six‐factor no‐confounding designs. (7th January 2021)

• Record Type: Journal Article
• Authors: Metcalfe, Carly E.; Jones, Bradley; Montgomery, Douglas C.
• Editors: Medlin, Rebecca M.
• Is Part Of: Quality and reliability engineering international. Volume 37:Number 7(2021)
• Page Start: 3055
• Page End: 3065
• Publication Date: 2021-01-07
• Subjects: alias patterns -- model selection -- nonregular designs -- orthogonal designs -- screening experiments; Reliability (Engineering) -- Periodicals; Quality control -- Periodicals; High technology -- Periodicals; 620.00452
• DOI: 10.1002/qre.2831
• Languages: English
• ISSNs: 0748-8017

Abstract

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.