Design of follow‐up experiments for improving model discrimination and parameter estimation

One goal of experimentation is to identify which design parameters most significantly influence the mean performance of a system. Another goal is to obtain good parameter estimates for a response model that quantifies how the mean performance depends on influential parameters. Most experimental design techniques focus on one goal at a time. This paper proposes a new entropy‐based design criterion for follow‐up experiments that jointly identifies the important parameters and reduces the variance of parameter estimates. We simplify computations for the normal linear model by identifying an approximation that leads to a closed form solution. The criterion is applied to an example from the experimental design literature, to a known model and to a critical care facility simulation experiment. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004