### Abstract

Original language | English |
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Pages (from-to) | 1-16 |

Journal | Statistics and Computing |

DOIs | |

Publication status | E-pub ahead of print - 21 Aug 2019 |

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### Cite this

*Statistics and Computing*, 1-16. https://doi.org/10.1007/s11222-019-09889-7

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*Statistics and Computing*, pp. 1-16. https://doi.org/10.1007/s11222-019-09889-7

**Estimation of relative risk for events on a linear network.** / Baddeley, Adrian; Nair, Gopalan; McSwiggan, Gregory.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Estimation of relative risk for events on a linear network

AU - Baddeley, Adrian

AU - Nair, Gopalan

AU - McSwiggan, Gregory

PY - 2019/8/21

Y1 - 2019/8/21

N2 - 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.

AB - 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.

UR - http://www.mendeley.com/research/estimation-relative-risk-events-linear-network

U2 - 10.1007/s11222-019-09889-7

DO - 10.1007/s11222-019-09889-7

M3 - Article

SP - 1

EP - 16

JO - Statistics and Computing

JF - Statistics and Computing

SN - 0960-3174

ER -