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This paper considers sparse spiked covariance matrix models in the high-dimensional setting and studies the minimax estimation of the covariance matrix and the principal subspace as well as the minimax rank detection.
The rotation of eigenvectors by a perturbation. III
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Optimal detection of sparse principal components in high dimension
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Complexity theoretic lower bounds for sparse principal component detection
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Asymptotics of the principal components estimator of large factor models with weak factors
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Signal detection in high dimension: The multispiked case
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