Worst case asymptotics of power-weighted Euclidean functionals

AbstractLet A,|A|⩽n, be a subset of [0,1]d, and let L(A,[0,1]d,p) be the length of the minimal matching, the minimal spanning tree, or the traveling salesman problem on A with weight function w(e)=|e|p. In the case 1⩽p<d, Yukich (Combinatorica 16 (1996) 575) obtained the asymptotic of αL(n,d,p)=maxA⊂[0,1]d,|A|⩽nL(A,[0,1]d,p). In this paper we extend his result to the whole range 0<p<∞

Stability and integration over Bergman metrics

We study partition functions of random Bergman metrics, with the actions defined by a class of geometric functionals known as `stability functions'. We introduce a new stability invariant - the critical value of the coupling constant - defined as the minimal coupling constant for which the partition function converges. It measures the minimal degree of stability of geodesic rays in the space the Bergman metrics, with respect to the action. We calculate this critical value when the action is the $\nu$-balancing energy, and show that $\gamma_k^{\rm crit} = k - h$ on a Riemann surface of genus $h$.Comment: 24 pages, 3 figure

A Riemannian-Stein Kernel Method

This paper presents a theoretical analysis of numerical integration based on interpolation with a Stein kernel. In particular, the case of integrals with respect to a posterior distribution supported on a general Riemannian manifold is considered and the asymptotic convergence of the estimator in this context is established. Our results are considerably stronger than those previously reported, in that the optimal rate of convergence is established under a basic Sobolev-type assumption on the integrand. The theoretical results are empirically verified on $\mathbb{S}^2$

Connected Spatial Networks over Random Points and a Route-Length Statistic

We review mathematically tractable models for connected networks on random points in the plane, emphasizing the class of proximity graphs which deserves to be better known to applied probabilists and statisticians. We introduce and motivate a particular statistic $R$ measuring shortness of routes in a network. We illustrate, via Monte Carlo in part, the trade-off between normalized network length and $R$ in a one-parameter family of proximity graphs. How close this family comes to the optimal trade-off over all possible networks remains an intriguing open question. The paper is a write-up of a talk developed by the first author during 2007--2009.Comment: Published in at http://dx.doi.org/10.1214/10-STS335 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org