Logistic regression for spatial Gibbs point processes
We propose a computationally efficient technique, based on logistic regression, for fittingGibbs point process models to spatial point pattern data. The score of the logistic regression is anunbiased estimating function and is closely related to the pseudolikelihood score. Implementationof our techn...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
Oxford University Press
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/39331 |
| _version_ | 1848755562414604288 |
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| author | Baddeley, Adrian Coeurjolly, J. Rubak, E. Waagepetersen, R. |
| author_facet | Baddeley, Adrian Coeurjolly, J. Rubak, E. Waagepetersen, R. |
| author_sort | Baddeley, Adrian |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | We propose a computationally efficient technique, based on logistic regression, for fittingGibbs point process models to spatial point pattern data. The score of the logistic regression is anunbiased estimating function and is closely related to the pseudolikelihood score. Implementationof our technique does not require numerical quadrature, and thus avoids a source of bias inherentin other methods. For stationary processes, we prove that the parameter estimator is stronglyconsistent and asymptotically normal, and propose a variance estimator. We demonstrate theefficiency and practicability of the method on a real dataset and in a simulation study. |
| first_indexed | 2025-11-14T08:58:17Z |
| format | Journal Article |
| id | curtin-20.500.11937-39331 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:58:17Z |
| publishDate | 2014 |
| publisher | Oxford University Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-393312017-09-13T14:24:48Z Logistic regression for spatial Gibbs point processes Baddeley, Adrian Coeurjolly, J. Rubak, E. Waagepetersen, R. Georgii–Nguyen–Zessin formula Exponential family model Pseudolikelihood Logistic regression Estimating function We propose a computationally efficient technique, based on logistic regression, for fittingGibbs point process models to spatial point pattern data. The score of the logistic regression is anunbiased estimating function and is closely related to the pseudolikelihood score. Implementationof our technique does not require numerical quadrature, and thus avoids a source of bias inherentin other methods. For stationary processes, we prove that the parameter estimator is stronglyconsistent and asymptotically normal, and propose a variance estimator. We demonstrate theefficiency and practicability of the method on a real dataset and in a simulation study. 2014 Journal Article http://hdl.handle.net/20.500.11937/39331 10.1093/biomet/ast060 Oxford University Press restricted |
| spellingShingle | Georgii–Nguyen–Zessin formula Exponential family model Pseudolikelihood Logistic regression Estimating function Baddeley, Adrian Coeurjolly, J. Rubak, E. Waagepetersen, R. Logistic regression for spatial Gibbs point processes |
| title | Logistic regression for spatial Gibbs point processes |
| title_full | Logistic regression for spatial Gibbs point processes |
| title_fullStr | Logistic regression for spatial Gibbs point processes |
| title_full_unstemmed | Logistic regression for spatial Gibbs point processes |
| title_short | Logistic regression for spatial Gibbs point processes |
| title_sort | logistic regression for spatial gibbs point processes |
| topic | Georgii–Nguyen–Zessin formula Exponential family model Pseudolikelihood Logistic regression Estimating function |
| url | http://hdl.handle.net/20.500.11937/39331 |