Abstract #300143

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JSM 2003 Abstract #300143
Activity Number: 124
Type: Contributed
Date/Time: Monday, August 4, 2003 : 10:30 AM to 12:20 PM
Sponsor: SSC
Abstract - #300143
Title: Discrete M-Robust Designs for Regression Models
Author(s): Julie Zhou*+
Companies: University of Victoria
Address: Dept. of Mathematics & Statistics, Victoria, BC, V8W 3P4, Canada
Keywords: robust regression design ; optimal design ; change of bias function ; change of variance function ; annealing algorithm
Abstract:

M-robust designs are defined and constructed on a discrete design space for misspecified linear regression models with possibly autocorrelated errors. These designs minimize the mean squared errors if the linear regression models are correct with uncorrelated errors, subject to two robust constraints which control the change of bias and the change of variance under model departures. In particular, the change of bias and the change of variance functions are introduced and an M-robust criterion is proposed to study robust designs. Results are obtained for the errors following the q-th order moving average processes. Simulated annealing algorithm is applied to construct M-robust designs. Examples are given to show the M-robust designs and to compare them with minimax robust designs.

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