Three-level main-effects designs exploiting prior information about model uncertainty

DOI10.1016/j.jspi.2006.01.003zbMath1103.62072OpenAlexW2056457264MaRDI QIDQ861231

Steven G. Gilmour, Pi-Wen Tsai, Roger Mead

Publication date: 9 January 2007

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2006.01.003

zbMATH Keywords

projectionsorthogonal arraysprior informationfactor screeningBayesian optimal designs

Mathematics Subject Classification ID

Optimal statistical designs (62K05) Bayesian inference (62F15) Factorial statistical designs (62K15)

Related Items (2)

Screening designs for model discrimination ⋮ Constructing two-level \(Q_B\)-optimal screening designs using mixed-integer programming and heuristic algorithms

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Geometric isomorphism and minimum aberration for factorial designs with quantitative factors
• Box-Cox Transformations and the Taguchi Method
• A Simple Bayesian Modification of D-Optimal Designs to Reduce Dependence on an Assumed Model
• Follow-Up Designs to Resolve Confounding in Multifactor Experiments
• A Bayesian Variable-Selection Approach for Analyzing Designed Experiments with Complex Aliasing
• Projective three-level main effects designs robust to model uncertainty
• Box-Cox Transformations and the Taguchi Method: An Alternative Analysis of a Taguchi Case Study
• Bayesian variable selection with related predictors

This page was built for publication: Three-level main-effects designs exploiting prior information about model uncertainty