Expected L2-discrepancy bound for a class of new stratified sampling models
Abstract
We introduce a class of convex equivolume partitions. Expected L2-discrepancy are discussed under these partitions. There are two main results. First, under this kind of partitions, we generate random point sets with smaller expected L2-discrepancy than classical jittered sampling for the same sampling number. Second, an explicit expected L2-discrepancy upper bound under this kind of partitions is also given. Further, among these new partitions, there is optimal expected L2-discrepancy upper bound.
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