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Main Authors: Fend, Chiara, Redenbach, Claudia
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2404.10594
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author Fend, Chiara
Redenbach, Claudia
author_facet Fend, Chiara
Redenbach, Claudia
contents In spatial statistics, point processes are often assumed to be isotropic meaning that their distribution is invariant under rotations. Statistical tests for the null hypothesis of isotropy found in the literature are based either on asymptotics or on Monte Carlo simulation of a parametric null model. Here, we present a nonparametric test based on resampling the Fry points of the observed point pattern. Empirical levels and powers of the test are investigated in a simulation study for four point process models with anisotropy induced by different mechanisms. Finally, a real data set is tested for isotropy.
format Preprint
id arxiv_https___arxiv_org_abs_2404_10594
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Nonparametric Isotropy Test for Spatial Point Processes using Random Rotations
Fend, Chiara
Redenbach, Claudia
Methodology
In spatial statistics, point processes are often assumed to be isotropic meaning that their distribution is invariant under rotations. Statistical tests for the null hypothesis of isotropy found in the literature are based either on asymptotics or on Monte Carlo simulation of a parametric null model. Here, we present a nonparametric test based on resampling the Fry points of the observed point pattern. Empirical levels and powers of the test are investigated in a simulation study for four point process models with anisotropy induced by different mechanisms. Finally, a real data set is tested for isotropy.
title Nonparametric Isotropy Test for Spatial Point Processes using Random Rotations
topic Methodology
url https://arxiv.org/abs/2404.10594