Testing first-order intensity model in non-homogeneous Poisson point processes with covariates

TL;DR

This paper introduces a new statistical test for verifying if the intensity function of a non-homogeneous Poisson process depends on a covariate, supported by theoretical proofs, simulations, and real data applications.

Contribution

It proposes a novel $L^2$-distance based test for the covariate-dependent intensity model, including asymptotic analysis and bootstrap calibration methods.

Findings

Test effectively detects covariate dependence in intensity functions.

Simulation results demonstrate good size and power properties.

Real data applications illustrate practical utility.

Abstract

Modelling the first-order intensity function is one of the main aims in point process theory, and it has been approached so far from different perspectives. One appealing model describes the intensity as a function of a spatial covariate. In the recent literature, estimation theory and several applications have been developed assuming this model, but without formally checking this assumption. In this paper we address this problem for a non-homogeneous Poisson point process, by proposing a new test based on an $L^2$-distance. We also prove the asymptotic normality of the statistic and we suggest a bootstrap procedure to accomplish the calibration. Two applications with real data are presented and a simulation study to better understand the performance of our proposals is accomplished. Finally some possible extensions of the present work to non-Poisson processes and to a multi-dimensional covariate context are detailed.