Convergence and prediction of principal component scores in high-dimensional settings

DOI10.1214/10-AOS821zbMath1204.62097arXiv1211.2970WikidataQ42702618 ScholiaQ42702618MaRDI QIDQ620562

Seunggeun Lee, Fred A. Wright, Fei Zou

Publication date: 19 January 2011

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1211.2970

zbMATH Keywords

random matrixPCAPC regressionPC scores

Mathematics Subject Classification ID

Related Items (16)

Isolation-by-distance-and-time in a stepping-stone model ⋮ Convergence of Sample Eigenvectors of Spiked Population Model ⋮ Adjusting systematic bias in high dimensional principal component scores ⋮ Statistical inference for high-dimension, low-sample-size data ⋮ Multidimensional scaling of noisy high dimensional data ⋮ Quantile regression estimation of partially linear additive models ⋮ Modelling functional additive quantile regression using support vector machines approach ⋮ Reconstruction of a low-rank matrix in the presence of Gaussian noise ⋮ PCA consistency for the power spiked model in high-dimensional settings ⋮ Boundary behavior in high dimension, low sample size asymptotics of PCA ⋮ \(e\)PCA: high dimensional exponential family PCA ⋮ Detection, reconstruction, and characterization algorithms from noisy data in multistatic wave imaging ⋮ Targeted Random Projection for Prediction From High-Dimensional Features ⋮ Asymptotic properties of principal component analysis and shrinkage-bias adjustment under the generalized spiked population model ⋮ Effective PCA for high-dimension, low-sample-size data with noise reduction via geometric representations ⋮ Computation of ancestry scores with mixed families and unrelated individuals

Uses Software

• PLINK
• Eigenstrat

Cites Work

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• Principal component analysis.
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• On Consistency and Sparsity for Principal Components Analysis in High Dimensions
• Geometric Representation of High Dimension, Low Sample Size Data
• The high-dimension, low-sample-size geometric representation holds under mild conditions
• Additive Risk Models for Survival Data with High‐Dimensional Covariates
• DISTRIBUTION OF EIGENVALUES FOR SOME SETS OF RANDOM MATRICES
• Asymptotic Theory for Principal Component Analysis
• Prediction by Supervised Principal Components
• Principal Components
• On the Sampling Theory of Roots of Determinantal Equations

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