Robust designs for series estimation

We discuss optimal design problems for a popular method of series estimation in regression problems. Commonly used design criteria are based on the generalized variance of the estimates of the coefficients in a truncated series expansion and do not take possible bias into account. We present a general perspective of constructing robust and efficient designs for series estimators which is based on the integrated mean squared error criterion. A minimax approach is used to derive designs which are robust with respect to deviations caused by the bias and the possibility of heteroscedasticity. A special case results from the imposition of an unbiasedness constraint; the resulting "unbiased designs" are particularly simple, and easily implemented. Our results are illustrated by constructing robust designs for series estimation with spherical harmonic descriptors, Zernike polynomials and Chebyshev polynomials. c 2008 Elsevier B.V. All rights reserved.

• Type: Journal
• Year: 2008
• Where: CSDA
• Authors: Holger Dette, Douglas P. Wiens
• Keywords: CSDA 2008, Designs, Optimal Design Problems, Series Estimation