Joint variable selection of both fixed and random effects for Gaussian process-based spatially varying coefficient models
| Autor: | Dambon, Jakob A, Sigrist, Fabio, Furrer, Reinhard |
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| Přispěvatelé: | University of Zurich, Dambon, Jakob A |
| Rok vydání: | 2022 |
| Předmět: |
Planning and Development
FOS: Computer and information sciences Geography Geography Planning and Development Library and Information Sciences 1710 Information Systems based optimizationpenalized maximum likelihood estimationspatial statistics Methodology (stat.ME) 10123 Institute of Mathematics 510 Mathematics 3305 Geography Planning and Development 10231 Institute for Computational Science 3309 Library and Information Sciences Information Systems Adaptive LASSOBayesian optimizationcoordinate descent algorithmmodel Statistics - Methodology Information Systems |
| Zdroj: | International Journal of Geographical Information Science. 36:2525-2548 |
| ISSN: | 1362-3087 1365-8816 |
| DOI: | 10.1080/13658816.2022.2097684 |
| Popis: | Spatially varying coefficient (SVC) models are a type of regression model for spatial data where covariate effects vary over space. If there are several covariates, a natural question is which covariates have a spatially varying effect and which not. We present a new variable selection approach for Gaussian process-based SVC models. It relies on a penalized maximum likelihood estimation (PMLE) and allows variable selection both with respect to fixed effects and Gaussian process random effects. We validate our approach both in a simulation study as well as a real world data set. Our novel approach shows good selection performance in the simulation study. In the real data application, our proposed PMLE yields sparser SVC models and achieves a smaller information criterion than classical MLE. In a cross-validation applied on the real data, we show that sparser PML estimated SVC models are on par with ML estimated SVC models with respect to predictive performance. Comment: 26 pages including appendix. Containing 6 figures and 6 tables. Updated Declarations |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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