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Abstract Details
Activity Number:
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470
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Physical and Engineering Sciences
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Abstract - #301849 |
Title:
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Designs for Discriminating Between Linear and Cubic Regression Models
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Author(s):
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Santanu Dutta*+ and Subir Ghosh
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Companies:
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University of California at Riverside and University of California at Riverside
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Address:
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Department of Statistics, Riverside, CA, 92521,
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Keywords:
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Model Discrimination ;
T-optimality ;
I and J criteria ;
Linear and Cubic Models ;
Optimal Design
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Abstract:
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We consider the identification and discrimination between two competing regression models, a linear and a cubic, with the response variable y and the explanatory variable x. The four distinct design points x1, x2, x3, and x4 are replicated n1, n2, n3, and n4 times respectively, satisfying n1 + n2 + n3 + n4 = n, n1 = n4 and n2 = n3. For a fixed value of n, we compare designs optimum with respect to two criteria, J (equivalent to T-optimality) and a proposed I. We obtain a class of designs that are better than the Dette-Titoff T-optimal (equivalently J-optimal) design (Dette and Titoff (2009)) under the criterion I in their setup where the quadratic coefficient is zero in the cubic model. However, the Dette-Titoff design is better than our class of designs under the criterion J. We also obtain optimal designs when the quadratic coefficient is not zero.
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