1 citations · 1 across the 3 of their papers we have counts for
3 papers
Principal component analysis balancing prediction and approximation accuracy for spatial data
Si Cheng, Magali N. Blanco, Timothy V. Larson +3
Dimension reduction is often the first step in statistical modeling or prediction of multivariate spatial data. However, most existing dimension reduction techniques do not account…
Variable importance measure for spatial machine learning models with application to air pollution exposure prediction
Si Cheng, Magali N. Blanco, Lianne Sheppard +2
Exposure assessment is fundamental to air pollution cohort studies. The objective is to predict air pollution exposures for study subjects at locations without data in order to opt…
A novel principal component analysis for spatially-misaligned multivariate air pollution data
Roman A. Jandarov, Lianne A. Sheppard, Paul D. Sampson +1
We propose novel methods for predictive (sparse) PCA with spatially misaligned data. These methods identify principal component loading vectors that explain as much variability in…