Optimal Design of Multifactor Experiments via Grid Exploration
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For computing efficient approximate designs of multifactor experiments, we propose a simple algorithm based on adaptive exploration of the grid of all combinations of factor levels. We demonstrate that the algorithm significantly outperforms several state-of-the-art competitors for problems with discrete, continuous, as well as mixed factors. Importantly, we provide a free R code that permits direct verification of the numerical results and allows the researchers to easily compute optimal or nearly-optimal experimental designs for their own statistical models.
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