Fast Kernel Smoothing of Point Patterns on a Large Network using Two-dimensional Convolution
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Other documents of the author: rakshit, suman; Davies, Tilman; Moradi, Mehdi; McSwiggan, Greg; Nair, Gopalan; Mateu, Jorge; Baddeley, Adrian
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Show full item recordcomunitat-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 |
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Title
Fast Kernel Smoothing of Point Patterns on a Large Network using Two-dimensional ConvolutionAuthor (s)
Date
2019-06Publisher
WileyBibliographic citation
RAKSHIT, Suman, et al. Fast Kernel Smoothing of Point Patterns on a Large Network using Two‐dimensional Convolution. International Statistical Review.Type
info:eu-repo/semantics/articlePublisher version
https://onlinelibrary.wiley.com/doi/full/10.1111/insr.12327Version
info:eu-repo/semantics/publishedVersionSubject
Abstract
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. [-]
Investigation project
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)Rights
© 2019 The Authors. International Statistical Review © 2019 International Statistical Institute. Published by John Wiley & Sons.
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