当前位置:
X-MOL 学术
›
Qual. Reliab. Eng. Int.
›
论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Aliased informed model selection strategies for six-factor no-confounding designs
Quality and Reliability Engineering International ( IF 2.3 ) Pub Date : 2021-01-07 , DOI: 10.1002/qre.2831 Carly E. Metcalfe 1 , Bradley Jones 2 , Douglas C. Montgomery 1
Quality and Reliability Engineering International ( IF 2.3 ) Pub Date : 2021-01-07 , DOI: 10.1002/qre.2831 Carly E. Metcalfe 1 , Bradley Jones 2 , Douglas C. Montgomery 1
Affiliation
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.
中文翻译:
六因素无混杂设计的别名知情模型选择策略
非常规设计是常规分辨率 IV 设计的首选替代方案,因为它们避免了混杂的双因素交互作用。因此,非常规设计可以估计和识别一些活跃的双因素交互作用。然而,由于非常规设计有时具有复杂的别名结构,标准筛选策略可能无法识别所有主动效应。在本文中,我们探索了具有正交主效应的特定无混杂六因素 16 次运行非常规设计。通过利用我们对别名结构的了解,我们可以通知模型选择过程。我们的别名知情模型选择 (AIMS) 策略是一种特定于设计的方法,我们将其与三种通用模型选择方法进行比较;逐步回归、套索和 Dantzig 选择器。
更新日期:2021-01-07
中文翻译:
六因素无混杂设计的别名知情模型选择策略
非常规设计是常规分辨率 IV 设计的首选替代方案,因为它们避免了混杂的双因素交互作用。因此,非常规设计可以估计和识别一些活跃的双因素交互作用。然而,由于非常规设计有时具有复杂的别名结构,标准筛选策略可能无法识别所有主动效应。在本文中,我们探索了具有正交主效应的特定无混杂六因素 16 次运行非常规设计。通过利用我们对别名结构的了解,我们可以通知模型选择过程。我们的别名知情模型选择 (AIMS) 策略是一种特定于设计的方法,我们将其与三种通用模型选择方法进行比较;逐步回归、套索和 Dantzig 选择器。