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Local moment matching: A unified methodology for symmetric functional estimation and distribution estimation under Wasserstein distance
[article]
2018
arXiv
pre-print
We present Local Moment Matching (LMM), a unified methodology for symmetric functional estimation and distribution estimation under Wasserstein distance. We construct an efficiently computable estimator that achieves the minimax rates in estimating the distribution up to permutation, and show that the plug-in approach of our unlabeled distribution estimator is "universal" in estimating symmetric functionals of discrete distributions. Instead of doing best polynomial approximation explicitly as
arXiv:1802.08405v2
fatcat:o2t4oveyd5djpkscnpt3igh3zm