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.
Stationary random measures: Covariance asymptotics, variance bounds and central limit theorems
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Introduces p-uniformity for fluctuation scaling and proves its preservation under transport, enabling new isotropic p-uniform point processes with high p that simulate in linear time.
For random normal matrices, the scaled variance of eigenvalue count in an interior Borel set A converges to a boundary integral of sqrt(ΔQ) with respect to Hausdorff measure; a similar result holds near the droplet edge using harmonic measure.
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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Persistence of asymptotic variance under transport: from hyperfluctuation to stealthy hyperuniformity
Introduces p-uniformity for fluctuation scaling and proves its preservation under transport, enabling new isotropic p-uniform point processes with high p that simulate in linear time.
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Universality for fluctuations of counting statistics of random normal matrices
For random normal matrices, the scaled variance of eigenvalue count in an interior Borel set A converges to a boundary integral of sqrt(ΔQ) with respect to Hausdorff measure; a similar result holds near the droplet edge using harmonic measure.