Estimation of relative risk for events on a linear network

Greg McSwiggan, Adrian Baddeley, Gopalan Nair

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Motivated by the study of traffic accidents on a road network, we discuss the estimation of the relative risk, the ratio of rates of occurrence of different types of events occurring on a network of lines. Methods developed for two-dimensional spatial point patterns can be adapted to a linear network, but their requirements and performance are very different on a network. Computation is slow and we introduce new techniques to accelerate it. Intensities (occurrence rates) are estimated by kernel smoothing using the heat kernel on the network. The main methodological problem is bandwidth selection. Binary regression methods, such as likelihood cross-validation and least squares cross-validation, perform tolerably well in our simulation experiments, but the Kelsall–Diggle density-ratio cross-validation method does not. We find a theoretical explanation, and propose a modification of the Kelsall–Diggle method which has better performance. The methods are applied to traffic accidents in a regional city, and to protrusions on the dendritic tree of a neuron.
Original languageEnglish
Pages (from-to)469-484
Number of pages16
JournalStatistics and Computing
Volume30
Issue number2
Early online date21 Aug 2019
DOIs
Publication statusPublished - Mar 2020

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