A PC-based decomposition of FVE into low- and high-dimensional components reduces bias when applying GWASH or LMM-REML to strongly correlated high-dimensional predictors.
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PCA-derived global factor from gray-matter isotropic volume fraction in NODDI data correlates with specific cognitive scores in HCP Young Adult cohort.
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Principal Components Decomposition of Fraction of Variance Explained in High Dimensional Linear Models with Strong Correlation
A PC-based decomposition of FVE into low- and high-dimensional components reduces bias when applying GWASH or LMM-REML to strongly correlated high-dimensional predictors.