Variable selection using penalised likelihoods for point patterns on a linear network.
Motivated by the analysis of a comprehensive database of road traffic accidents, we investigate methods of variable selection for spatial point process models on a linear network. The original data may include explanatory spatial covariates, such as road curvature, and ‘mark’ variables attributed to...
| Main Authors: | , , , |
|---|---|
| Format: | Journal Article |
| Language: | English |
| Published: |
Wiley-Blackwell
2021
|
| Subjects: | |
| Online Access: | http://purl.org/au-research/grants/arc/DP130102322 http://hdl.handle.net/20.500.11937/91582 |
| _version_ | 1848765554751438848 |
|---|---|
| author | Rakshit, Suman McSwiggan, Greg Nair, Gopalan Baddeley, Adrian |
| author_facet | Rakshit, Suman McSwiggan, Greg Nair, Gopalan Baddeley, Adrian |
| author_sort | Rakshit, Suman |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Motivated by the analysis of a comprehensive database of road traffic accidents, we investigate methods of variable selection for spatial point process models on a linear network. The original data may include explanatory spatial covariates, such as road curvature, and ‘mark’ variables attributed to individual accidents, such as accident severity. The treatment of mark variables is new. Variable selection is applied to the canonical covariates, which may include spatial covariate effects, mark effects and mark-covariate interactions. We approximate the likelihood of the point process model by that of a generalised linear model, in such a way that spatial covariates and marks are both associated with canonical covariates. We impose a convex penalty on the log likelihood, principally the elastic-net penalty, and maximise the penalised loglikelihood by cyclic coordinate ascent. A simulation study compares the performances of the lasso, ridge regression and elastic-net methods of variable selection on their ability to select variables correctly, and on their bias and standard error. Standard techniques for selecting the regularisation parameter γ often yielded unsatisfactory results. We propose two new rules for selecting γ which are designed to have better performance. The methods are tested on a small dataset on crimes in a Chicago neighbourhood, and applied to a large dataset of road traffic accidents in Western Australia. |
| first_indexed | 2025-11-14T11:37:06Z |
| format | Journal Article |
| id | curtin-20.500.11937-91582 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:37:06Z |
| publishDate | 2021 |
| publisher | Wiley-Blackwell |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-915822023-05-17T04:44:33Z Variable selection using penalised likelihoods for point patterns on a linear network. Rakshit, Suman McSwiggan, Greg Nair, Gopalan Baddeley, Adrian Science & Technology Physical Sciences Statistics & Probability Mathematics discretised models elastic net generalised linear model lasso Poisson process SPATIAL STATISTICAL-MODELS 2ND-ORDER ANALYSIS REGULARIZATION EQUIVALENCE REGRESSION GRAPHS LASSO Motivated by the analysis of a comprehensive database of road traffic accidents, we investigate methods of variable selection for spatial point process models on a linear network. The original data may include explanatory spatial covariates, such as road curvature, and ‘mark’ variables attributed to individual accidents, such as accident severity. The treatment of mark variables is new. Variable selection is applied to the canonical covariates, which may include spatial covariate effects, mark effects and mark-covariate interactions. We approximate the likelihood of the point process model by that of a generalised linear model, in such a way that spatial covariates and marks are both associated with canonical covariates. We impose a convex penalty on the log likelihood, principally the elastic-net penalty, and maximise the penalised loglikelihood by cyclic coordinate ascent. A simulation study compares the performances of the lasso, ridge regression and elastic-net methods of variable selection on their ability to select variables correctly, and on their bias and standard error. Standard techniques for selecting the regularisation parameter γ often yielded unsatisfactory results. We propose two new rules for selecting γ which are designed to have better performance. The methods are tested on a small dataset on crimes in a Chicago neighbourhood, and applied to a large dataset of road traffic accidents in Western Australia. 2021 Journal Article http://hdl.handle.net/20.500.11937/91582 10.1111/anzs.12341 English http://purl.org/au-research/grants/arc/DP130102322 Wiley-Blackwell fulltext |
| spellingShingle | Science & Technology Physical Sciences Statistics & Probability Mathematics discretised models elastic net generalised linear model lasso Poisson process SPATIAL STATISTICAL-MODELS 2ND-ORDER ANALYSIS REGULARIZATION EQUIVALENCE REGRESSION GRAPHS LASSO Rakshit, Suman McSwiggan, Greg Nair, Gopalan Baddeley, Adrian Variable selection using penalised likelihoods for point patterns on a linear network. |
| title | Variable selection using penalised likelihoods for point patterns on a linear network. |
| title_full | Variable selection using penalised likelihoods for point patterns on a linear network. |
| title_fullStr | Variable selection using penalised likelihoods for point patterns on a linear network. |
| title_full_unstemmed | Variable selection using penalised likelihoods for point patterns on a linear network. |
| title_short | Variable selection using penalised likelihoods for point patterns on a linear network. |
| title_sort | variable selection using penalised likelihoods for point patterns on a linear network. |
| topic | Science & Technology Physical Sciences Statistics & Probability Mathematics discretised models elastic net generalised linear model lasso Poisson process SPATIAL STATISTICAL-MODELS 2ND-ORDER ANALYSIS REGULARIZATION EQUIVALENCE REGRESSION GRAPHS LASSO |
| url | http://purl.org/au-research/grants/arc/DP130102322 http://hdl.handle.net/20.500.11937/91582 |