Normal approximation for statistics of Gibbsian input in geometric probability

DOI10.1239/aap/1449859795zbMath1333.60037arXiv1409.6380OpenAlexW2218786666MaRDI QIDQ2786421

Aihua Xia, Joseph E. Yukich

Publication date: 12 February 2016

Published in: Advances in Applied Probability (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1409.6380

zbMATH Keywords

Stein's methodmaximal pointsnormal approximationGibbs point processrandom Euclidean graphsinsurance modelsspatial birth-growth model

Mathematics Subject Classification ID

Related Items (7)

A functional central limit theorem for the empirical Ripley's \(K\)-function ⋮ Testing goodness of fit for point processes via topological data analysis ⋮ Normal approximation in total variation for statistics in geometric probability ⋮ Persistent homology based goodness-of-fit tests for spatial tessellations ⋮ Convergence rate for geometric statistics of point processes having fast decay of dependence ⋮ Decorrelation of a class of Gibbs particle processes and asymptotic properties of U-statistics ⋮ Palm theory, random measures and Stein couplings

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