Vafaei, Nafiseh
- Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences
Abstract
Determinantal point processes (dpps) are widely used for modeling repulsive behavior in spatial point patterns, yet their extension to spatio-temporal settings has received little attention. This paper develops a comprehensive framework for spatio-temporal dpps (stdpps) based on covariance and spectral representations. We establish fundamental existence conditions; derive closed-form expressions for first- and second-order summary functions-including the pair correlation function, spectral density, and space-time K-function; and introduce both separable and non separable covariance structures that capture spatial, temporal, and joint space-time inhibition. The theoretical developments are illustrated through simulated examples, which highlight how spatial and temporal range parameters govern the strength and extent of repulsion. Together, these results provide a flexible and computationally tractable framework for modeling regularity in space-time point process data.
Keywords
Covariance function; regularity; simulation; spatio-temporal point process; spectral density
Published in
Communications in Statistics - Theory and Methods
2026
Publisher: TAYLOR AND FRANCIS INC
SLU Authors
Ghorbani, Mohammad
- Department of Biosystems and Technology, Swedish University of Agricultural Sciences
- Department of Biosystems and Technology, Swedish University of Agricultural Sciences
UKÄ Subject classification
Probability Theory and Statistics
Publication identifier
- DOI: https://doi.org/10.1080/03610926.2026.2631039
Permanent link to this page (URI)
https://res.slu.se/id/publ/146782