We consider functionals measuring the dispersion of a d-dimensional distribution which are based on the volumes of simplices of dimension k ≤ d formed by k + 1 independent copies and raised to some power δ. We study properties of extremal measures that maximize these functionals. In particular, for positive δ we characterize their support and for negative δ we establish connection with potentia...

We give analytic methods for nonparametric bias reduction that remove the need for computationally intensive methods like the bootstrap and the jackknife. We call an estimate pth order if its bias has magnitude n 0 as n0 → ∞, where n0 is the sample size (or the minimum sample size if the estimate is a function of more than one sample). Most estimates are only first order and require O(N) calcul...

The problem of accurate nonparametric estimation of distributional functionals (integral functionals of one or more probability distributions) has received recent interest due to their wide applicability in signal processing, information theory, machine learning, and statistics. In particular, k-nearest neighbor (nn) based methods have received a lot of attention due to their adaptive nature an...

If a weakly compact convex set K in a real Banach space X is strongly exposed by a dense set of functionals in X', it is proved that the functionals which expose K form a residual set in A". If v. & -* X is a measure, it follows that the set of exposing functionals of its range is a residual Gs in A". This, in turn, is found to be equivalent to a theorem of B. Walsh on the residuality of functi...

This paper estimates upper and lower bounds for the approximation rates of iterated Boolean sums of multivariate Bernstein polynomials. Both direct and inverse inequalities for the approximation rate are established in terms of a certain K -functional. From these estimates, one can also determine the class of functions yielding optimal approximations to the iterated Boolean sums. c © 2008 Elsev...

We give analytic methods for nonparametric bias reduction that remove the need for computationally intensive methods like the bootstrap and the jackknife. We call an estimate pth order if its bias has magnitude n 0 as n0 → ∞, where n0 is the sample size (or the minimum sample size if the estimate is a function of more than one sample). Most estimates are only first order and require O(N) calcul...

The first point here is that convex sets can be separated by linear functionals. Second, continuous linear functionals on subspaces of a locally convex topological vectorspace have continuous extensions to the whole space. Proofs are for real vectorspaces. The complex versions are corollaries. A crucial corollary is that on locally convex topological vectorspaces continuous linear functionals s...

Given a closed (and non necessarily compact) basic semialgebraic set K ⊆ R, we solve the K-moment problem for continuous linear functionals. Namely, we introduce a weighted l1-norm lw on R[x], and show that the lw-closures of the preordering P and quadratic module Q (associated with the generators of K) is the cone Psd(K) of polynomials nonnegative on K. We also prove that P and Q solve the K-m...