Goodness-of-fit test for point processes first-order intensity
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Abstract
Modelling the first-order intensity function is one of the main aims in point process theory. An appropriate model describes the first-order intensity as a nonparametric function of spatial covariates. A formal testing procedure is presented to assess the goodness-of-fit of this model, assuming an inhomogeneous Poisson point process. The test is based on a quadratic distance between two kernel intensity estimators. The asymptotic normality of the test statistic is proved and a bootstrap procedure to approximate its distribution is suggested. The proposal is illustrated with two applications to real data sets, and an extensive simulation study to evaluate its finite-sample performance.
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Borrajo, González-Manteiga, & Martínez-Miranda. (2024). Goodness-of-fit test for point processes first-order intensity. Computational Statistics and Data Analysis, 194. https://doi.org/10.1016/J.CSDA.2024.107929
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The authors are grateful for the very constructive comments from the three anonymous reviewers and Associate Editor which helped to improve this manuscript. The authors acknowledge the support through the project PID2020-116587GB-I00 granted by MICIU/AEI/10.13039/501100011033. The authors also acknowledge the Canadian Wildland Fire Information System for their activity in recording and freely providing part of the real data used in this paper.
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© 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license







