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Pritam Ranjan

Statistics Building Cohen Room, 230
Acadia University

Space-filling designs are widely used for emulating computer simulators. Over the last three decades, a wide spectrum of Latin hypercube designs (LHDs) has been proposed with different space-filling criteria like minimum correlation among factors, maximin inter-point distance, and orthogonality among the factors. In this talk I will present a new class of space-filling designs. These designs are derived from randomization defining contrast subspaces (RDCSSs) in two-level factorial experiments. It turns out that the different overlapping structures among the RDCSSs lead to the construction of orthogonal and nearly orthogonal arrays, which further generate space-filling designs. We focus on space-filling designs generated from NOAs that are derived from stars (a set of RDCSSs with a common overlap).

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