Diagonally Dominant Principal Component Analysis
DD-HC test
tools:::Rd_package_title("ddpca")
Diagonally Dominant Principal Component Analysis using Convex approach
Diagonally Dominant Principal Component Analysis using Nonconvex appro...
Higher Criticism for detecting rare and weak signals
IHC-DD test
Projection onto the Diagonally Dominant Cone
Projection onto the Symmetric Diagonally Dominant Cone
Efficient procedures for fitting the DD-PCA (Ke et al., 2019, <arXiv:1906.00051>) by decomposing a large covariance matrix into a low-rank matrix plus a diagonally dominant matrix. The implementation of DD-PCA includes the convex approach using the Alternating Direction Method of Multipliers (ADMM) and the non-convex approach using the iterative projection algorithm. Applications of DD-PCA to large covariance matrix estimation and global multiple testing are also included in this package.