Aliased informed model selection strategies for six-factor no-confounding designs

Carly E. Metcalfe, Bradley Jones, Douglas C. Montgomery

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)3055-3065
Number of pages11
JournalQuality and Reliability Engineering International
Issue number7
StatePublished - Nov 2021


  • alias patterns
  • model selection
  • nonregular designs
  • orthogonal designs
  • screening experiments

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Management Science and Operations Research


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