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Criterion-robust optimal designs for model discrimination and parameter estimation in Fourier regression models

✍ Scribed by Mei-Mei Zen; Min-Hsiao Tsai

Publisher
Elsevier Science
Year
2004
Tongue
English
Weight
283 KB
Volume
124
Category
Article
ISSN
0378-3758

No coin nor oath required. For personal study only.

✦ Synopsis

Consider the problem of discriminating between two competitive Fourier regression on the circle [ -; ] and estimating parameters in the models. To ΓΏnd designs which are e cient for both model discrimination and parameter estimation, Zen and Tsai (some criterion-robust optimal designs for the dual problem of model discrimination and parameter estimation, Indian J. Statist. 64, 322-338) proposed a multiple-objective optimality criterion for polynomial regression models. In this work, taking the same M -criterion, which puts weight (0 6 6 1) for model discrimination and 1 -for parameter estimation, and using the techniques of projection design, the corresponding M -optimal design for Fourier regression models is explicitly derived in terms of canonical moments. The behavior of the M -optimal designs is investigated under di erent weighted selection criterion. And the extreme value of the minimum M -e ciency of any M -optimal design is obtained at = * , which results in the M * -optimal design to be served as a criterion-robust optimal design for the problem.

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