Local composite likelihood for spatial point processes
© 2017 Elsevier B.V.We develop a general approach to spatial inhomogeneity in the analysis of spatial point pattern data. The ideas of local likelihood (or 'geographically weighted regression') are applied to the composite likelihoods that are commonly used for spatial point processes. For...
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| Format: | Journal Article |
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2016
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| Online Access: | http://purl.org/au-research/grants/arc/DP130104470 http://hdl.handle.net/20.500.11937/52852 |
| _version_ | 1848759027113132032 |
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| author | Baddeley, Adrian |
| author_facet | Baddeley, Adrian |
| author_sort | Baddeley, Adrian |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2017 Elsevier B.V.We develop a general approach to spatial inhomogeneity in the analysis of spatial point pattern data. The ideas of local likelihood (or 'geographically weighted regression') are applied to the composite likelihoods that are commonly used for spatial point processes. For Poisson point processes, local likelihood is already known; for Gibbs point processes we develop a local version of Besag's pseudolikelihood; for Cox point processes and Neyman-Scott cluster processes we develop a local version of the Palm likelihood of Ogata and Katsura. Using recent results for composite likelihood and for spatial point processes, we develop tools for statistical inference, including intensity approximations, variance estimators, localised tests for the significance of a covariate effect, and global tests of homogeneity. Computationally efficient approximations are available using the Fast Fourier Transform. We develop methods for bandwidth selection, which may also be useful for smoothing dependent spatial data. There are mathematical connections to existing exploratory methods such as the scan statistic, local indicators of spatial association, and point process residuals. The methods are demonstrated on three example datasets, and R code is supplied. |
| first_indexed | 2025-11-14T09:53:21Z |
| format | Journal Article |
| id | curtin-20.500.11937-52852 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:53:21Z |
| publishDate | 2016 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-528522022-10-12T02:42:03Z Local composite likelihood for spatial point processes Baddeley, Adrian © 2017 Elsevier B.V.We develop a general approach to spatial inhomogeneity in the analysis of spatial point pattern data. The ideas of local likelihood (or 'geographically weighted regression') are applied to the composite likelihoods that are commonly used for spatial point processes. For Poisson point processes, local likelihood is already known; for Gibbs point processes we develop a local version of Besag's pseudolikelihood; for Cox point processes and Neyman-Scott cluster processes we develop a local version of the Palm likelihood of Ogata and Katsura. Using recent results for composite likelihood and for spatial point processes, we develop tools for statistical inference, including intensity approximations, variance estimators, localised tests for the significance of a covariate effect, and global tests of homogeneity. Computationally efficient approximations are available using the Fast Fourier Transform. We develop methods for bandwidth selection, which may also be useful for smoothing dependent spatial data. There are mathematical connections to existing exploratory methods such as the scan statistic, local indicators of spatial association, and point process residuals. The methods are demonstrated on three example datasets, and R code is supplied. 2016 Journal Article http://hdl.handle.net/20.500.11937/52852 10.1016/j.spasta.2017.03.001 http://purl.org/au-research/grants/arc/DP130104470 restricted |
| spellingShingle | Baddeley, Adrian Local composite likelihood for spatial point processes |
| title | Local composite likelihood for spatial point processes |
| title_full | Local composite likelihood for spatial point processes |
| title_fullStr | Local composite likelihood for spatial point processes |
| title_full_unstemmed | Local composite likelihood for spatial point processes |
| title_short | Local composite likelihood for spatial point processes |
| title_sort | local composite likelihood for spatial point processes |
| url | http://purl.org/au-research/grants/arc/DP130104470 http://hdl.handle.net/20.500.11937/52852 |