Adjusting systematic bias in high dimensional principal component scores

DOI10.5705/ss.202019.0400OpenAlexW3173158825MaRDI QIDQ5066782

Sungkyu Jung

Publication date: 30 March 2022

Published in: Statistica Sinica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.5705/ss.202019.0400

zbMATH Keywords

principal component analysisjackknifeHDLSS

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Unnamed Item
• Unnamed Item
• Asymptotic properties of principal component analysis and shrinkage-bias adjustment under the generalized spiked population model
• Exploratory factor analysis -- parameter estimation and scores prediction with high-dimensional data
• Boundary behavior in high dimension, low sample size asymptotics of PCA
• Convergence and prediction of principal component scores in high-dimensional settings
• PCA consistency in high dimension, low sample size context
• Asymptotics of empirical eigenstructure for high dimensional spiked covariance
• The statistics and mathematics of high dimension low sample size asymptotics
• Convergence of sample eigenvalues, eigenvectors, and principal component scores for ultra-high dimensional data
• Sure Independence Screening for Ultrahigh Dimensional Feature Space
• A survey of high dimension low sample size asymptotics
• Embracing the Blessing of Dimensionality in Factor Models
• On Consistency and Sparsity for Principal Components Analysis in High Dimensions
• When and Why are Principal Component Scores a Good Tool for Visualizing High‐dimensional Data?
• On the number of principal components in high dimensions
• Asymptotic Theory for Principal Component Analysis
• Large Covariance Estimation by Thresholding Principal Orthogonal Complements