MINIMAL SPANNING TREES AND STEIN'S METHOD

• 成果类型: Article
• 署名作者: Chatterjee, Sourav; Sen, Sanchayan
• 署名单位: Stanford University; McGill University
• 刊物名称: ANNALS OF APPLIED PROBABILITY
• ISSN/ISSBN: 1050-5164
• DOI: 10.1214/16-AAP1239
• 发表日期: 2017
• 页码: 1588-1645
• 关键词: CENTRAL-LIMIT-THEOREM; continuum percolation; normal approximations; infinite cluster; SCALING LIMITS; uniqueness; GROWTH; rates

摘要

Kesten and Lee [Ann. AppL Probab. 6 (1996) 495-527] proved that the total length of a minimal spanning tree on certain random point configurations in R-d satisfies a central limit theorem. They also raised the question: how to make these results quantitative? Error estimates in central limit theorems satisfied by many other standard functionals studied in geometric probability are known, but techniques employed to tackle the problem for those functionals do not apply directly to the minimal spanning tree. Thus, the problem of determining the convergence rate in the central limit theorem for Euclidean minimal spanning trees has remained open. In this work, we establish bounds on the convergence rate for the Poissonized version of this problem by using a variation of Stein's method. We also derive bounds on the convergence rate for the analogous problem in the setup of the lattice Z(d). The contribution of this paper is twofold. First, we develop a general technique to compute convergence rates in central limit theorems satisfied by minimal spanning trees on sequences of weighted graphs, including minimal spanning trees on Poisson points inside a sequence of growing cubes. Second, we present a way of quantifying the Burton Keane argument for the uniqueness of the infinite open cluster. The latter is interesting in its own right and based on a generalization of our technique, Duminil-Copin, Ioffe and Velenik [Ann. Probab. 44 (2016) 3335-3356] have recently obtained bounds on probability of two-arm events in a broad class of translation-invariant percolation models.