Generalized spatial regression with differential regularization Article Swipe
Matthieu Wilhelm
,
Laura M. Sangalli
·
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.1080/00949655.2016.1182532
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.1080/00949655.2016.1182532
We aim at analysing geostatistical and areal data observed over irregularly shaped spatial domains and having a distribution within the exponential family. We propose a generalized additive model that allows to account for spatially varying covariate information. The model is fitted by maximizing a penalized log-likelihood function, with a roughness penalty term that involves a differential quantity of the spatial field, computed over the domain of interest. Efficient estimation of the spatial field is achieved resorting to the finite element method, which provides a basis for piecewise polynomial surfaces. The proposed model is illustrated by an application to the study of criminality in the city of Portland, OR, USA.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1080/00949655.2016.1182532
- OA Status
- green
- Cited By
- 16
- References
- 38
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W2104002570Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1080/00949655.2016.1182532Digital Object Identifier
- Title
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Generalized spatial regression with differential regularizationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2016Year of publication
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2016-05-10Full publication date if available
- Authors
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Matthieu Wilhelm, Laura M. SangalliList of authors in order
- Landing page
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https://doi.org/10.1080/00949655.2016.1182532Publisher landing page
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://hdl.handle.net/11311/1002807Direct OA link when available
- Concepts
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Mathematics, Spatial analysis, Piecewise, Applied mathematics, Covariate, Functional data analysis, Regularization (linguistics), Mathematical optimization, Statistics, Mathematical analysis, Artificial intelligence, Computer scienceTop concepts (fields/topics) attached by OpenAlex
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16Total citation count in OpenAlex
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2025: 1, 2024: 3, 2023: 3, 2021: 4, 2020: 1Per-year citation counts (last 5 years)
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38Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.analysing | 3 |
| abstract_inverted_index.covariate | 35 |
| abstract_inverted_index.function, | 46 |
| abstract_inverted_index.interest. | 66 |
| abstract_inverted_index.penalized | 44 |
| abstract_inverted_index.piecewise | 86 |
| abstract_inverted_index.resorting | 75 |
| abstract_inverted_index.roughness | 49 |
| abstract_inverted_index.spatially | 33 |
| abstract_inverted_index.surfaces. | 88 |
| abstract_inverted_index.estimation | 68 |
| abstract_inverted_index.maximizing | 42 |
| abstract_inverted_index.polynomial | 87 |
| abstract_inverted_index.application | 96 |
| abstract_inverted_index.criminality | 101 |
| abstract_inverted_index.exponential | 20 |
| abstract_inverted_index.generalized | 25 |
| abstract_inverted_index.illustrated | 93 |
| abstract_inverted_index.irregularly | 10 |
| abstract_inverted_index.differential | 55 |
| abstract_inverted_index.distribution | 17 |
| abstract_inverted_index.information. | 36 |
| abstract_inverted_index.geostatistical | 4 |
| abstract_inverted_index.log-likelihood | 45 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.5799999833106995 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.69755394 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |