title: Convergence of Sample Eigenvalues, Eigenvectors, and Principal Component Scores for Ultra-High Dimensional Data.
abstract: The development of high-throughput biomedical technologies has led to increased interest in the analysis of high-dimensional data where the number of features is much larger than the sample size. In this paper, we investigate principal component analysis under the ultra-high dimensional regime, where both the number of features and the sample size increase as the ratio of the two quantities also increases. We bridge the existing results from the finite and the high-dimension low sample size regimes, embedding the two regimes in a more general framework. We also numerically demonstrate the universal application of the results from the finite regime.
authors.affiliation: Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, Michigan 48109, U.S.A.
authors.forename: Seunggeun
authors.initials: S
authors.lastname: Lee
authors.affiliation: Department of Biostatistics, University of North Carolina, 135 Dauer Drive, Chapel Hill, North Carolina 27599, U.S.A.
authors.forename: Fei
authors.initials: F
authors.lastname: Zou
authors.affiliation: Bioinformatics Research Center, North Carolina State University, 1 Lampe Drive, Raleigh, North Carolina 27607, U.S.A.
authors.forename: Fred A
authors.initials: FA
authors.lastname: Wright
keywords: High-Dimension Low Sample Size Data; Principal Component Analysis; Random Matrix