Estimation of relative risk for events on a linear network
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 networ...
| Main Authors: | , , |
|---|---|
| Format: | Journal Article |
| Language: | English |
| Published: |
SPRINGER
2020
|
| Subjects: | |
| Online Access: | http://purl.org/au-research/grants/arc/DP130102322 http://hdl.handle.net/20.500.11937/91580 |
| _version_ | 1848765554203033600 |
|---|---|
| author | McSwiggan, G. Baddeley, Adrian Nair, G. |
| author_facet | McSwiggan, G. Baddeley, Adrian Nair, G. |
| author_sort | McSwiggan, G. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:37:06Z |
| format | Journal Article |
| id | curtin-20.500.11937-91580 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:37:06Z |
| publishDate | 2020 |
| publisher | SPRINGER |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-915802023-05-16T06:49:59Z Estimation of relative risk for events on a linear network McSwiggan, G. Baddeley, Adrian Nair, G. Science & Technology Technology Physical Sciences Computer Science, Theory & Methods Statistics & Probability Computer Science Mathematics Bandwidth selection Cross-validation Dendritic spines Density ratio Heat kernel Kelsall-Diggle cross-validation Road traffic accidents KERNEL DENSITY-ESTIMATION BANDWIDTH SELECTION CROSS-VALIDATION NONPARAMETRIC-ESTIMATION SPATIAL VARIATION POINT PATTERNS REGRESSION MATRICES DISEASE 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. 2020 Journal Article http://hdl.handle.net/20.500.11937/91580 10.1007/s11222-019-09889-7 English http://purl.org/au-research/grants/arc/DP130102322 http://purl.org/au-research/grants/arc/DP130104470 SPRINGER fulltext |
| spellingShingle | Science & Technology Technology Physical Sciences Computer Science, Theory & Methods Statistics & Probability Computer Science Mathematics Bandwidth selection Cross-validation Dendritic spines Density ratio Heat kernel Kelsall-Diggle cross-validation Road traffic accidents KERNEL DENSITY-ESTIMATION BANDWIDTH SELECTION CROSS-VALIDATION NONPARAMETRIC-ESTIMATION SPATIAL VARIATION POINT PATTERNS REGRESSION MATRICES DISEASE McSwiggan, G. Baddeley, Adrian Nair, G. Estimation of relative risk for events on a linear network |
| title | Estimation of relative risk for events on a linear network |
| title_full | Estimation of relative risk for events on a linear network |
| title_fullStr | Estimation of relative risk for events on a linear network |
| title_full_unstemmed | Estimation of relative risk for events on a linear network |
| title_short | Estimation of relative risk for events on a linear network |
| title_sort | estimation of relative risk for events on a linear network |
| topic | Science & Technology Technology Physical Sciences Computer Science, Theory & Methods Statistics & Probability Computer Science Mathematics Bandwidth selection Cross-validation Dendritic spines Density ratio Heat kernel Kelsall-Diggle cross-validation Road traffic accidents KERNEL DENSITY-ESTIMATION BANDWIDTH SELECTION CROSS-VALIDATION NONPARAMETRIC-ESTIMATION SPATIAL VARIATION POINT PATTERNS REGRESSION MATRICES DISEASE |
| url | http://purl.org/au-research/grants/arc/DP130102322 http://purl.org/au-research/grants/arc/DP130102322 http://hdl.handle.net/20.500.11937/91580 |