Fast Kernel Smoothing of Point Patterns on a Large Network using Two-dimensional Convolution
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Otros documentos de la autoría: rakshit, suman; Davies, Tilman; Moradi, Mehdi; McSwiggan, Greg; Nair, Gopalan; Mateu, Jorge; Baddeley, Adrian
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Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
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https://doi.org/10.1111/insr.12327 |
Metadatos
Título
Fast Kernel Smoothing of Point Patterns on a Large Network using Two-dimensional ConvolutionAutoría
Fecha de publicación
2019-06Editor
WileyCita bibliográfica
RAKSHIT, Suman, et al. Fast Kernel Smoothing of Point Patterns on a Large Network using Two‐dimensional Convolution. International Statistical Review.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://onlinelibrary.wiley.com/doi/full/10.1111/insr.12327Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
We propose a computationally efficient and statistically principled method for kernel smoothingof point pattern data on a linear network. The point locations, and the network itself, are convolvedwith a two-dimensional ... [+]
We propose a computationally efficient and statistically principled method for kernel smoothingof point pattern data on a linear network. The point locations, and the network itself, are convolvedwith a two-dimensional kernel and then combined into an intensity function on the network. Thiscan be computed rapidly using the fast Fourier transform, even on large networks and for largebandwidths, and is robust against errors in network geometry. The estimator is consistent, and itsstatistical efficiency is only slightly suboptimal. We discuss bias, variance, asymptotics, bandwidthselection, variance estimation, relative risk estimation and adaptive smoothing. The methods areused to analyse spatially varying frequency of traffic accidents in Western Australia and the relativerisk of different types of traffic accidents in Medellín, Colombia. [-]
Proyecto de investigación
Australian Research Council (grants DP 130102322 andDP 130104470) ; European Union GEO-C (projec thttp:// www.geo-c.eu/(Moradi:H2020-MSCA-ITN-2014, grant agreement 642332) ; Royal Society of New Zealand,Marsden Fund (Fast Start grant 15-UOO-092) ; Grains Research and DevelopmentCorporation, Australia (SAGI-3 project); Spanish Ministry of Science and Education (grant MTM2016-78917-R)Derechos de acceso
© 2019 The Authors. International Statistical Review © 2019 International Statistical Institute. Published by John Wiley & Sons.
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http://rightsstatements.org/vocab/InC/1.0/
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