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stat.ME2026
Spatial prediction of environmental processes using random forests: How best to account for spatial dependence?
Duncan Lee, Vinny Davies, Helen R. Savage +3
Geostatistical spatial prediction for environmental processes is typically undertaken using Gaussian process models via Kriging, while machine learning (ML) algorithms are state-of…
stat.ME2026
A spatial random forest algorithm for population-level epidemiological risk assessment
Duncan Lee, Vinny Davies
Spatial epidemiology identifies the drivers of elevated population-level disease risks, using disease counts, exposures and known confounders at the areal unit level. Poisson regre…
stat.ME2023
Conditional autoregressive models fused with random forests to improve small-area spatial prediction
Cara MacBride, Vinny Davies, Duncan Lee
In areal unit data with missing or suppressed data, it desirable to create models that are able to predict observations that are not available. Traditional statistical methods achi…