most citedPrincipal component analysis balancing prediction and approximation accuracy for spatial data

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stat.ME2024★ 1 cited

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…

stat.ME2015

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…