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Abstract Details
Activity Number:
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511
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Computing
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Abstract - #300548 |
Title:
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Optimal Designs for Rational Function Regression
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Author(s):
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David Papp*+
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Companies:
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Rutgers University
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Address:
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, Piscataway, NJ, 08854,
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Keywords:
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Optimal design ;
Rational function regression ;
Semidefinite programming ;
Nonlinear regression
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Abstract:
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We consider optimal non-sequential designs for a large class of (linear and nonlinear) regression models involving polynomials and rational functions with heteroscedastic noise also given by a polynomial or rational weight function. The proposed method generates a polynomial whose zeros are the support points of the optimal approximate design, and generalizes a number of previously known results of the same flavor. The method is based on a mathematical optimization model that can incorporate various criteria of optimality and can be solved very efficiently by well established numerical optimization methods. In contrast to previous optimization-based methods proposed for similar design problems, it also has theoretical guarantee of both its convergence and its algorithmic efficacy. As a corollary, an upper bound on the size of the support set of the minimally-supported optimal designs is also found.
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