Robust Experimental Design for Multivariate Generalized Linear Models

A simple heuristic is proposed for constructing robust experimental designs for multivariate generalized linear models. The method is based on clustering a set of local optimal designs. A method for finding local D-optimal designs using available resources is also introduced. Clustering, with its simplicity and minimal computation needs, is demonstrated to outperform more complex and sophisticated methods.