Bootstrap bandwidth selection for the pair correlation function of inhomogeneous spatial point processes
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Otros documentos de la autoría: Fuentes-Santos, Isabel; González-Manteiga, Wenceslao; Mateu, Jorge
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
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INVESTIGACIONMetadatos
Título
Bootstrap bandwidth selection for the pair correlation function of inhomogeneous spatial point processesFecha de publicación
2023-06-13Editor
Taylor and Francis GroupISSN
0094-9655; 1563-5163Cita bibliográfica
I. Fuentes-Santos, W. González-Manteiga & J. Mateu (2023) Bootstrap bandwidth selection for the pair correlation function of inhomogeneous spatial point processes, Journal of Statistical Computation and Simulation, 93:18, 3329-3361. DOI: 10.1080/00949655.2023.2220860Tipo de documento
info:eu-repo/semantics/articleVersión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
This work focuses on kernel estimation of the pair correlation function (PCF) for inhomogeneous spatial point processes. We propose a bootstrap bandwidth selector based on minimizing the mean integrated squared error ... [+]
This work focuses on kernel estimation of the pair correlation function (PCF) for inhomogeneous spatial point processes. We propose a bootstrap bandwidth selector based on minimizing the mean integrated squared error (MISE). The variance term is estimated by nonparametric bootstrap, and the bias by a plug-in approach using a pilot estimator of the PCF. Kernel estimators of the PCF also require a pilot estimator of the first-order intensity. We test the performance of the bandwidth selector and the role of the pilot intensity estimator in a simulation study. The bootstrap bandwidth selector is competitive with cross-validation procedures, but the contribution of the bandwidth parameter to the goodness-of-fit of the kernel PCF estimator is minor in comparison with that of the pilot intensity function. The data-based kernel intensity estimator leads to biased kernel PCF estimators, while both kernel and parametric covariate-based intensities provide accurate estimators of the PCF. [-]
Entidad financiadora
Agencia Estatal de Investigación (AEI)/FEDER, UE
Código del proyecto o subvención
MTM2016-76969-P | PID2019-107392RB-I00 | PID2020-116587GB-I00
Derechos de acceso
© 2023 The Author(s). Published by Informa
UK Limited, trading as Taylor & Francis
Group.
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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