Point Process Learning: a cross-validation-based statistical framework for point processes
Paper i proceeding, 2025

Recently, Point Process Learning was introduced as a powerful approach to fitting Papangelou conditional intensity models to point pattern data. This cross-validation-based statistical theory was shown to significantly outperform the state-of-the-art in the context of kernel intensity estimation. In this paper, we further illustrate its potential by showing that it outperforms the state-of-the-art when fitting a hard-core Gibbs model.

Gibbs processes

Prediction errors

Spatial statistics

Point Process Learning

Cross-Validation

Författare

Julia Jansson

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Ottmar Cronie

Chalmers, Matematiska vetenskaper

Mehdi Moradi

Umeå universitet

Christophe A.N. Biscio

Aalborg Universitet

Italian Statistical Society Series on Advances in Statistics

3059-2135 (ISSN) 3059-2143 (eISSN)

Vol. 52
978-3-031-64345-3 (ISBN)

The 52nd Scientific Meeting of the Italian Statistical Society
Bari, Italy,

Ämneskategorier

Sannolikhetsteori och statistik

Mer information

Senast uppdaterat

2024-11-28