Nonparametric First-Order Analysis of Spatial and Spatio-Temporal Point Processes
First-order characteristics are essential functions in point processes representing the distribution of events in the corresponding domain. For decades, the inconsistency of the first-order kernel intensity estimator has been an obstacle to perform inference in the point process context. In this wor...
| Autores: | , , |
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| Tipo de recurso: | capítulo de libro |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universidad de Santiago de Compostela (USC) |
| Repositorio: | Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela |
| Idioma: | inglés |
| OAI Identifier: | oai:minerva.usc.gal:10347/39242 |
| Acceso en línea: | https://hdl.handle.net/10347/39242 |
| Access Level: | acceso abierto |
| Palabra clave: | Nonparametric Statistics Spatial Statistics First-order intensity |
| Sumario: | First-order characteristics are essential functions in point processes representing the distribution of events in the corresponding domain. For decades, the inconsistency of the first-order kernel intensity estimator has been an obstacle to perform inference in the point process context. In this work, we develop different procedures to obtain consistent estimators of the first-order intensity function, and we also propose bootstrap procedures to define effective bandwidth selectors. Moreover, these innovations are used in three testing problems: the goodness-of-fit of an appealing model in the literature of point processes with covariates, the nonparametric comparison of first-order intensity functions and a separability test for spatio-temporal point process. We illustrate the above-mentioned procedures with two wildfire data sets in Galicia (NW Spain) and in Canada. |
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