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Spatio-temporal point patterns on linear networks: Pseudo-separable intensity estimation
University Jaume I, Castellon, Spain. (Department of Mathematics)ORCID iD: 0000-0002-2868-7604
Public University of Navarre, Pamplona, Spain. (Department of Statistics, Computer Science and Mathematics)ORCID iD: 0000-0003-3905-4498
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.ORCID iD: 0000-0002-6721-8608
2020 (English)In: Spatial Statistics, E-ISSN 2211-6753Article in journal (Refereed) Epub ahead of print
Abstract [en]

Aside from reviewing different intensity estimation schemes for point processes on linear networks, this paper introduces two Voronoi-based intensity estimation approaches for spatio-temporal linear network point processes. The first is a separable estimator, which is obtained as a scaled product of a resample-smoothed Voronoi intensity estimator on the linear network in question and another one on the time domain. The second one, which we refer to as a pseudo-separable resample-smoothed Voronoi intensity estimator, uses a slightly different thinning strategy. Through a simulation study we show that the latter performs slightly better than the former. We finally apply the latter estimator to a spatio-temporal traffic accident point pattern.

Place, publisher, year, edition, pages
Elsevier, 2020.
Keywords [en]
Intensity estimation, Linear network, Pseudo-separability, Resample-smoothing, Spatio-temporal point process, Voronoi estimator
National Category
Probability Theory and Statistics
Research subject
Mathematical Statistics
Identifiers
URN: urn:nbn:se:umu:diva-167195DOI: 10.1016/j.spasta.2019.100400OAI: oai:DiVA.org:umu-167195DiVA, id: diva2:1384831
Available from: 2020-01-10 Created: 2020-01-10 Last updated: 2020-01-13

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Cronie, Ottmar

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