Robust designs for Haar wavelet approximation models



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.