Robust designs for Haar wavelet approximation models

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Abstract

In this paper, we discuss the construction of robust designs for heteroscedastic wavelet regression models when the assumed models are possibly contaminated over two different neighbourhoods: G1 and G2. Our main findings are: (1) A recursive formula for constructing D-optimal designs under G1; (2) Equivalency of Q-optimal and A-optimal designs under both G1 and G2; (3) D-optimal robust designs under G2; and (4) Analytic forms for A- and Q-optimal robust design densities under G2. Several examples are given for the comparison, and the results demonstrate that our designs are efficient. Copyright © 2010 John Wiley & Sons, Ltd.

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