Bayesian geographically weighted regression and its application for local modeling of relationships between tree variables
Geographically weighted regression (GWR) has become popular in recent years to deal with spatial autocorrelation and heterogeneity in forestry and ecological data. However, researchers have realized that GWR has some limitations, such as correlated model coefficients across study areas, strong influ...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Italian Society of Silviculture and Forest Ecology (SISEF)
2018-10-01
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| Series: | iForest - Biogeosciences and Forestry |
| Subjects: | |
| Online Access: | https://iforest.sisef.org/contents/?id=ifor2574-011 |