A Simple Bayesian Modification of D-Optimal Designs to Reduce Dependence on an Assumed Model

D-optimal and other computer-generated experimental designs have been criticized for being too dependent on an assumed statistical model. To address this criticism, we introduce the notion of empirical models that have both primary and potential terms. Combining this idea with the Bayesian paradigm,...

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Bibliographic Details
Published in:Technometrics 1994-02, Vol.36 (1), p.37-47
Main Authors: DuMouchel, William, Jones, Bradley
Format: Article
Language:English
Subjects:
Algorithms
Bayesian networks
Computer aided design
Computer-generated designs
Estimators
Exact sciences and technology
Experiment design
Experimental design
Factorial design
Mathematics
Minimum-bias designs
Mixture designs
Modeling
Probability and statistics
Sample size
Sciences and techniques of general use
Standard deviation
Statistical discrepancies
Statistics
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Summary:D-optimal and other computer-generated experimental designs have been criticized for being too dependent on an assumed statistical model. To address this criticism, we introduce the notion of empirical models that have both primary and potential terms. Combining this idea with the Bayesian paradigm, this article proposes a modification of the D-optimal approach that preserves the flexibility and ease of use of algorithmic designs while being more resistant to the biases caused by an incorrect model. These designs provide a Bayesian justification for resolution IV designs. Several theoretical examples and a practical example from the literature demonstrate the advantages of the proposed method.
ISSN:0040-1706
1537-2723
DOI:10.1080/00401706.1994.10485399