A0304
Title: James-Stein Estimator of moderately-spiked leading eigenvector
Authors: Sungkyu Jung - Seoul National University (Korea, South) [presenting]
Abstract: Recently, a James-Stein shrinkage (JS) estimator has gained attention as a powerful tool for estimating the -leading eigenvector of covariance matrices. The efficacy of the JS estimator has been demonstrated under a strongly-spiked leading eigenvalue model, using the high-dimensional, low-sample-size (HDLSS) asymptotic regime, where the number of variables increases while the sample size remains fixed. We extend the application of the JS shrinkage to the regime of moderately-spiked leading eigenvalues and reveal a key condition involving a signal-to-noise ratio, for the JS estimator to be useful. Furthermore, we develop shrinkage estimators for principal component variance and scores, enabling their application in high-dimensional principal component analysis.
