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Activity Number:
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411
- Developments in the Construction of Experimental Designs
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Type:
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Contributed
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Date/Time:
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Tuesday, August 1, 2017 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Physical and Engineering Sciences
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Abstract #322593
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View Presentation
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Title:
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Bayesian D-Optimal Screening Experiments with Partial Replication
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Author(s):
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David Edwards* and Robert Leonard
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Companies:
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Virginia Commonwealth University and Miami University
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Keywords:
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Bayesian D-optimal ;
Model Selection ;
Power ;
Pure Error ;
Replication
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Abstract:
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Small screening designs are often used in the initial stages of experimentation with the goal of identifying important main effects as well as to gain insight on potentially important two-factor interactions. Common experimental designs for screening are unreplicated and as such, provide no unbiased estimate of experimental error. If statistical inference is considered an integral part of analysis, one view is that inferential procedures should be performed using the unbiased pure error estimate. As full replication of an experiment may be quite costly, partial replication offers an alternative for obtaining a model independent error estimate. Gilmour and Trinca (2012) introduce criteria for the design of experiments for statistical inference (providing for the optimal selection of replicated design points). We begin with an extension of their work by proposing a Bayesian criterion for the construction of partially replicated screening designs with less dependence on an assumed model. We then consider the use of the proposed criterion within the context of multi-criteria design selection where estimation and protection against model misspecification are considered.
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Authors who are presenting talks have a * after their name.
