Robust planning and analysis of experiments

Robust statistics and the design of experiments are two of the fastest growing fields in contemporary statistics. Up to now, there has been very little overlap between these fields. In robust statistics, robust alternatives to the nonrobust least squares estimator were developed, while in experimental design, designs for the efficient use of the least squares estimator were derived.

This volume is the first to link these two areas by studying the influence of the design on the efficiency and robustness of robust estimators and tests. It shows that robust statistical procedures profit by an appropriate choice of the design and that efficient designs for a robust statistical analysis are more applicable. The classical approaches of experimental design and robust statistics are introduced before the areas are linked.