Predicting the intensity of partially observed data from a revisited kriging for point processes

2016/04/20 by Edith Gabriel, Florent Bonneu, Gabriel, Edith +4
Mathematics · #FOS: Computer and information sciences #Methodology (stat.ME) #Morphological variations and asymmetry #Point processes and geometric inequalities

paper · pdf · doi:10.48550/arxiv.1604.05868

openalex publication_date 2016/04/20 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28

Abstract

We consider a stationary and isotropic spatial point process whose a realisation is observed within a large window. We assume it to be driven by a stationary random field U. In order to predict the local intensity of the point process, λ(x|U), we propose to define the first- and second-order characteristics of a random field, defined as the regularized counting process, from the ones of the point process and to interpolate the local intensity by using a kriging adapted to the regularized process.

Citations

• A Kernel Method for Smoothing Point Process Data

Related

• Predicting the intensity of partially observed data from a revisited kriging for point processes
• A Kriging procedure for processes indexed by graphs
• Spatial kriging for replicated temporal point processes
• Prediction of Spatial Point Processes: Regularized Method with\n Out-of-Sample Guarantees
• Regularization techniques for inhomogeneous (spatial) point processes intensity and conditional intensity estimation