Eliciting Factor Importance in a Designed Experiment

Recently, there has been great interest in the Bayes model for analyzing confounded designs. This model suggests that only a few of the main effects and interactions are "active" and estimates the posterior probability that a given factor is active. This article proposes using pairwise com...

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Published in:Technometrics 2001-05, Vol.43 (2), p.133-146
Main Authors: Grimshaw, Scott D, Collings, Bruce J, Larsen, Wayne A, Hurt, Carolyn R
Format: Article
Language:English
Subjects:
Bayesian analysis
Brainstorming
Design
Design analysis
Design engineering
Engineers
Experiment design
Graph theory
Graphics
Graphs
Guns
Pairwise comparisons
Pigments
Ranking
Refueling
Schedules
Screening designs
Software
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cited_by cdi_FETCH-LOGICAL-c347t-db22f1e06ca9875a1fcf6119daefdf0d3eb1ab12f8f273e6df33157f954e54403
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container_title Technometrics
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creator Grimshaw, Scott D
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description Recently, there has been great interest in the Bayes model for analyzing confounded designs. This model suggests that only a few of the main effects and interactions are "active" and estimates the posterior probability that a given factor is active. This article proposes using pairwise comparisons to elicit an expert's opinion and form a well-defined, coherent prior. The prior probability that a factor is active is modeled as a "preference" in the Bradley-Terry linear model for pairwise comparisons. This article provides suggested schedules that minimize the number of comparisons offered to the expert based on the expression of a comparison schedule as a graph theory problem. Examples demonstrate that an expert's knowledge can be obtained to adequate precision for the Bayes analysis of screening designs by asking a few simple questions.
doi_str_mv 10.1198/004017001750386251
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subjects Bayesian analysis
Brainstorming
Design
Design analysis
Design engineering
Engineers
Experiment design
Graph theory
Graphics
Graphs
Guns
Pairwise comparisons
Pigments
Ranking
Refueling
Schedules
Screening designs
Software
title Eliciting Factor Importance in a Designed Experiment
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