• Kalliopi Mylona, King's College London

  • Title: Optimal split-plot designs for precise pure-error estimation of the variance components

  • Abstract: In this work, we present a novel approach to design split-plot experiments which ensures that the two variance components can be estimated from pure error and guarantees a precise estimation of the response surface model. Our novel approach involves a new Bayesian compound D-optimal design criterion which pays attention to both the variance components and the fixed treatment effects. One part of the compound criterion (the part concerned with the treatment effects) is based on the response surface model of interest, while the other part (which is concerned with pure-error estimates of the variance components) is based on the full treatment model. We demonstrate that our new criterion yields split-plot designs that outperform existing designs from the literature both in terms of the precision of the pure-error estimates and the precision of the estimates of the factor effects.

This is a joint work with Steven G. Gilmour (King’s College London) and Peter Goos (KU Leuven)