Augmenting supersaturated designs with Bayesian D-optimality

Authors

• Alex J. Gutman, Air Force Institute of Technology
• Edward D. White, Air Force Institute of TechnologyFollow
• Dennis K. J. Lin, Pennsylvania State University - Main Campus
• Raymond R. Hill, Air Force Institute of Technology

Document Type

Article

Publication Date

3-2014

Abstract

A methodology is developed to add runs to existing supersaturated designs. The technique uses information from the analysis of the initial experiment to choose the best possible follow-up runs. After analysis of the initial data, factors are classified into one of three groups: primary, secondary, and potential. Runs are added to maximize a Bayesian D-optimality criterion to increase the information gained about those factors.

Comments

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DOI

10.1016/j.csda.2013.09.009

Source Publication

Computational Statistics & Data Analysis (ISSN 0167-9473)

Recommended Citation

Gutman, A. J., White, E. D., Lin, D. K. J., & Hill, R. R. (2014). Augmenting supersaturated designs with Bayesian D-optimality. Computational Statistics & Data Analysis, 71, 1147–1158. https://doi.org/10.1016/j.csda.2013.09.009