Statistical inference for principal components of spiked covariance matrices

DOI10.1214/21-AOS2143zbMath1486.62180arXiv2008.11903WikidataQ114060480 ScholiaQ114060480MaRDI QIDQ2131269

Yanyan Li

Publication date: 25 April 2022

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

Full work available at URL: https://arxiv.org/abs/2008.11903

zbMATH Keywords

eigenvectoradaptive estimatorrandom matrixsample covariance matrixspiked modelprincipal component

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Hypothesis testing in multivariate analysis (62H15) Random matrices (probabilistic aspects) (60B20) Eigenvalues, singular values, and eigenvectors (15A18) Random matrices (algebraic aspects) (15B52)

Related Items

Spiked multiplicative random matrices and principal components, Learning low-dimensional nonlinear structures from high-dimensional noisy data: an integral operator approach

Uses Software

• ISLR

Cites Work

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