Analysis of a supersaturated design using Entropy Prior Complexity for binary responses via generalized linear models |
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Institution: | 1. Micron School of Materials Science and Engineering, Boise State University, Engineering Building Suite 338, Boise, ID 83725, USA;2. Pilgrim Africa, 115 N 85th St #202, Seattle, WA 98103, USA;3. Institute of Health Metrics and Evaluation, University of Washington, 2301 Fifth Ave., Suite 600, Seattle, WA 98121, USA |
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Abstract: | A supersaturated design is a factorial design in which the number of effects to be estimated is greater than the available number of experimental runs. It is used in many experiments for screening purposes, i.e., for studying a large number of factors and then identifying the active ones. The goal with such a design is to identify just a few of the factors under consideration, that have dominant effects and to do this at minimum cost. While most of the literature on supersaturated designs has focused on the construction of designs and their optimality, the data analysis of such designs remains still at an early stage. In this paper, we incorporate the parameter model complexity into the supersaturated design analysis process, by assuming generalized linear models for a Bernoulli response, for analyzing main effects designs and discovering simultaneously the effects that are significant. |
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