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20242026
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stat.ME2026

Fitting the topology of synthetic particle systems with a novel graph representation

Martin Alexander Memmesheimer, Claudia Redenbach

The shape and arrangement of particles in a material determine its macroscopic properties. The generation of synthetic data with varying particle structure, often represented as 3D…

stat.ME2025

Goodness-of-fit tests for spatial point processes: A power study

Chiara Fend, Claudia Redenbach

Spatial point processes are used as models in many different fields ranging from ecology and forestry to cosmology and materials science. In recent years, model validation, and in…

stat.ME2025

Bayesian inference for Neyman-Scott point processes with anisotropic clusters

Jiří Dvořák, Emily Ewers, Tomáš Mrkvička +1

There are few inference methods available to accommodate covariate-dependent anisotropy in point process models. To address this, we propose an extended Bayesian MCMC approach for…

stat.ME2025

Goodness-of-fit tests for spatial point processes: A review

Chiara Fend, Claudia Redenbach

In this review, the state-of-the-art for goodness-of-fit testing for spatial point processes is summarized. Test statistics based on classical functional summary statistics and rec…

stat.ME2024

Nonparametric Isotropy Test for Spatial Point Processes using Random Rotations

Chiara Fend, Claudia Redenbach

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…