Develops a CLT framework for locally dependent scores on marked Euclidean point processes via geometric mixing and bounded-Lipschitz localization, with applications to spin systems and interacting particles.
Second-order Poincar\'e inequalities and localization on the Poisson space
2 Pith papers cite this work. Polarity classification is still indexing.
abstract
Given a mean zero functional $F$ of a Poisson measure on a metric space, we apply the Malliavin-Stein method to establish sharpened second-order Poincar\'e inequalities for $F/\sqrt{\operatorname{Var} (F)}$ in terms of fourth moments of difference operators. The rates of normal approximation are expressed in the Kolmogorov and Wasserstein distances and require fewer error terms than corresponding previous results. When $F$ is expressible as a sum of score functions which are distributionally close to scores having short-range structure, then we deduce that $F/\sqrt{\operatorname{Var}(F)}$ satisfies Berry-Esseen bounds. The normal approximation criteria of the scores, here called bounded Lipschitz localization, are more general than stabilization criteria and allow for unbounded interactions of scores. This approach yields Berry-Esseen bounds for local U-statistics on metric measures spaces, localizing functionals on hyperbolic space, as well as for Poisson functionals in a space-time setting, with infinite time horizon, including statistics of spatial birth-growth models and Laguerre tessellations.
fields
math.PR 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Derives Wasserstein bounds on normal approximation for sums of local scores in marked point processes with dependent marks via geometric weak mixing conditions.
citing papers explorer
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Limit theory for Lipschitz-localized statistics in random geometric models
Develops a CLT framework for locally dependent scores on marked Euclidean point processes via geometric mixing and bounded-Lipschitz localization, with applications to spin systems and interacting particles.
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Quantitative CLTs for Geometric Statistics of Dependent Marked Point Processes
Derives Wasserstein bounds on normal approximation for sums of local scores in marked point processes with dependent marks via geometric weak mixing conditions.