Follow-Up Designs to Resolve Confounding in Multifactor Experiments
From MaRDI portal
Publication:4365704
DOI10.2307/1271297zbMath0902.62088OpenAlexW2054223801MaRDI QIDQ4365704
R. D. Meyer, David M. Steinberg, G. E. P. Box
Publication date: 15 December 1998
Full work available at URL: https://doi.org/10.2307/1271297
Bayesian inference (62F15) Factorial statistical designs (62K15) Probabilistic methods, stochastic differential equations (65C99)
Related Items (23)
SIXTEEN RUN DESIGNS OF HIGH PROJECTIVITY FOR FACTOR SCREENING ⋮ Analysis of a supersaturated design using entropy prior complexity for binary responses via generalized linear models ⋮ Augmenting supersaturated designs with Bayesian \(D\)-optimality ⋮ KL-optimum designs: theoretical properties and practical computation ⋮ Level-screening designs for factors with many levels ⋮ Three-level main-effects designs exploiting prior information about model uncertainty ⋮ Model discrimination - another perspective on model-robust designs ⋮ Unnamed Item ⋮ Objective Bayesian model discrimination in follow-up experimental designs ⋮ Alternative optimal foldover plans for regular fractional factorial split-plot designs ⋮ Optimal foldover plans for blocked \(2^{m-k}\) fractional factorial designs ⋮ An integrative framework for geometric and hidden projections in three-level fractional factorial designs ⋮ Two-level supersaturated designs for \(2^k\) runs and other cases ⋮ A comparison of three approaches for constructing robust experimental designs ⋮ Recent developments in nonregular fractional factorial designs ⋮ Classification of efficient two-level fractional factorial designs of resolution IV or more ⋮ Some History Leading to Design Criteria for Bayesian Prediction ⋮ De-aliasing in two-level factorial designs: a Bayesian approach ⋮ Screening designs for model discrimination ⋮ Bayesian variable assessment ⋮ Active learning of continuous-time Bayesian networks through interventions* ⋮ Subdata selection algorithm for linear model discrimination ⋮ Genetic algorithms for MD-optimal follow-up designs
This page was built for publication: Follow-Up Designs to Resolve Confounding in Multifactor Experiments