Testing the first-order separability hypothesis for spatio-temporal point patterns

Mohammad Ghorbani, Nafiseh Vafaei, Jiří Dvořák and Mari Myllymäki

Computational Statistics & Data Analysis, 2021, vol. 161, issue C

Abstract: First-order separability of a spatio-temporal point process plays a fundamental role in the analysis of spatio-temporal point pattern data. While it is often a convenient assumption that simplifies the analysis greatly, existing non-separable structures should be accounted for in the model construction. Three different tests are proposed to investigate this hypothesis as a step of preliminary data analysis. The first two tests are exact or asymptotically exact for Poisson processes. The first test based on permutations and global envelopes allows one to detect at which spatial and temporal locations or lags the data deviate from the null hypothesis. The second test is a simple and computationally cheap χ2-test. The third test is based on stochastic reconstruction method and can be generally applied for non-Poisson processes. The performance of the first two tests is studied in a simulation study for Poisson and non-Poisson models. The third test is applied to the real data of the UK 2001 epidemic foot and mouth disease.1

Keywords: Global envelope; Log Gaussian Cox processes; Kernel estimation; Permutation; Separability of intensity function; Stochastic reconstruction (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:161:y:2021:i:c:s0167947321000797

DOI: 10.1016/j.csda.2021.107245

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