Are optimal designs poorer than rich designs in practice?
Oleg Volkov, Queen Mary University of London
A "rich" design requires many observations, spread over a dense grid of points.
An "optimal" design usually requires only a few. In theory, experimenters are better off with the latter design.
To see what might happen in practice, we consider pharmaceutical
experiments for parameter estimation of a nonlinear regression model.
These experiments illustrate the drawbacks, but also the promise, of optimal designs.
Hence, we examine compromise designs and other tools for improving designs in practice.