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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.doi:10.1198/004017006000000318 fatcat:ag5o5pytgfgzzd5uzruoht7cai