LIMIT THEORY FOR GEOMETRIC STATISTICS OF POINT PROCESSES HAVING FAST DECAY OF CORRELATIONS

• 成果类型: Article
• 署名作者: Blaszczyszyn, B.; Yogeshwaran, D.; Yukich, J. E.
• 署名单位: Indian Statistical Institute; Indian Statistical Institute Bangalore; Lehigh University
• 刊物名称: ANNALS OF PROBABILITY
• ISSN/ISSBN: 0091-1798
• DOI: 10.1214/18-AOP1273
• 发表日期: 2019
• 页码: 835-895
• 关键词: normal approximation; Moderate Deviations; gaussian limits; large numbers; functionals; zeros; Poisson; fluctuations; expansion; FIELDS

摘要

Let P be a simple, stationary point process on R d having fast decay of correlations, that is, its correlation functions factorize up to an additive error decaying faster than any power of the separation distance. Let P-n := P boolean AND W-n be its restriction to windows W-n := [- 1/2n(1/2), 1/2n(1/d)](d) subset of R-d. We consider the statistic := E-n(xi) := Sigma(x is an element of Pn) xi (x, P-n) where xi(x, P-n) denotes a score function representing the interaction of x with respect to P-n. When xi depends on local data in the sense that its radius of stabilization has an exponential tail, we establish expectation asymptotics, variance asymptotics and central limit theorems for and H-n(xi) more generally, for statistics of the re-scaled, possibly signed, 4-weighted point measures mu(xi)(n) := Sigma P-x is an element of(n) xi(x, P-n)delta(-1/d)(n)x, as W-n up arrow R-d. This gives the limit theory for nonlinear geometric statistics (such as clique counts, the number of Morse critical points, intrinsic volumes of the Boolean model and total edge length of the k-nearest neighbors graph) of alpha-determinantal point processes (for -1/alpha is an element of N) having fast decreasing kernels, including the beta-Ginibre ensembles, extending the Gaussian fluctuation results of Soshnikov [Ann. Probab. 30 (2002) 171-187] to nonlinear statistics. It also gives the limit theory for geometric U-statistics of alpha-permanental point processes (for 1/alpha is an element of N) as well as the zero set of Gaussian entire functions, extending the central limit theorems of Nazarov and Sodin [Comm. Math. Phys. 310 (2012) 75-98] and Shirai and Takahashi [J. Funct. Anal. 205 (2003) 414-463], which are also confined to linear statistics. The proof of the central limit theorem relies on a factorial moment expansion originating in [Stochastic Process. Appl. 56 (1995) 321-335; Statist. Probab. Lett. 36 (1997) 299-306] to show the fast decay of the correlations of 4-weighted point measures. The latter property is shown to imply a condition equivalent to Brillinger mixing, and consequently yields the asymptotic normality of pot via an extension of the cumulant method.