High dimensional deformed rectangular matrices with applications in matrix denoising
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Publication:2278666
DOI10.3150/19-BEJ1129zbMath1439.15013arXiv1702.06975MaRDI QIDQ2278666
Publication date: 5 December 2019
Published in: Bernoulli (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1702.06975
Random matrices (probabilistic aspects) (60B20) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Random matrices (algebraic aspects) (15B52)
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Statistical inference for principal components of spiked covariance matrices ⋮ Singular vector and singular subspace distribution for the matrix denoising model ⋮ Tracy-Widom law for the extreme eigenvalues of large signal-plus-noise matrices ⋮ Spiked multiplicative random matrices and principal components ⋮ High dimensional deformed rectangular matrices with applications in matrix denoising ⋮ Spiked separable covariance matrices and principal components ⋮ Optimality of spectral clustering in the Gaussian mixture model ⋮ Eigenvector distribution in the critical regime of BBP transition ⋮ Design-free estimation of integrated covariance matrices for high-frequency data ⋮ Edge statistics of large dimensional deformed rectangular matrices ⋮ Spiked sample covariance matrices with possibly multiple bulk components
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Cites Work
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