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Let $X,X_1,\dots, X_n$ be i.i.d.
Perturbation Theory for Linear Operators
T. Kato · 1980
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Asymptotic theory for the principal component analysis of a vector random function: some applications to statistical inference
J. Dauxois, A. Pousse, and Y. Romain · 1982
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Asymptotics of spectral projections of some random matrices approximating integral operators
V. Koltchinskii · 1998
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On the distribution of the largest eigenvalue in principal components analysis
I.M. Johnstone · 2001
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Inference for density families using functional principal component analysis
A. Kneip and K.J. Utikal · 2001
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Asymptotics of sample eigenstructure for a large dimensional spiked covariance model
D. Paul · 2007
Cited alongside, same era.
On consistency and sparsity for principal components analysis in high dimensions
I.M. Johnstone and A.Y. Lu · 2009
Cited alongside, same era.
F. Bunea and L. Xiao · 2012
Cited alongside, same era.
Introduction to the non-asymptotic analysis of random matrices
R. Vershynin · 2012
Cited alongside, same era.
Minimax rates of estimation for sparse PCA in high dimensionspca in high dimensions
V. Vu and J. Lei · 2012
Cited alongside, same era.
Concentration inequalities and moment bounds for sample covariance operators
V. Koltchinskii and K. Lounici
Cited in the paper.
Minimax bounds for sparse PCA with noisy high-dimensional data
A. Birnbaum, I.M. Johnstone, B. Nadler, and D. Paul · 2013
Later among the works it cites.
Sparse PCA: Optimal rates and adaptive estimation
T.T. Cai, Z. Ma, and Y. Wu · 2013
Later among the works it cites.
Sparse principal component analysis with missing observations
K. Lounici · 2013
Later among the works it cites.
V. Koltchinskii and K. Lounici · 2014
Later among the works it cites.
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