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Generalized Geographically Weighted Regression Model within a Modularized Bayesian Framework*
Geographically weighted regression (GWR) models handle geographical dependence through a spatially varying coefficient model and have been widely used in applied science, but its general Bayesian extension is unclear because it involves a weighted log-likelihood which does not imply a probability di...
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| Pubblicato in: | Bayesian Anal |
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| Autori principali: | , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7614111/ https://ncbi.nlm.nih.gov/pubmed/36714467 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1214/22-BA1357 |
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