Abstract
Given an i.i.d. sample from a probability measure P on ℝ, an estimator is constructed that efficiently estimates P in the bounded-Lipschitz metric for weak convergence of probability measures, and, at the same time, estimates the density of P — if it exists (but without assuming it does) — at the best possible rate of convergence in total variation loss (that is, in L1-loss for densities).
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