46 citations · 48 across the 2 of their papers we have counts for
2 papers
stat.AP2015★ 46 cited
Longitudinal high-dimensional principal components analysis with application to diffusion tensor imaging of multiple sclerosis
Vadim Zipunnikov, Sonja Greven, Haochang Shou +3
We develop a flexible framework for modeling high-dimensional imaging data observed longitudinally. The approach decomposes the observed variability of repeatedly measured high-dim…
stat.ME2014★ 2 cited
Fast, Exact Bootstrap Principal Component Analysis for p>1 million
Aaron Fisher, Brian Caffo, Brian Schwartz +1
Many have suggested a bootstrap procedure for estimating the sampling variability of principal component analysis (PCA) results. However, when the number of measurements per subjec…