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Factor analysis and principal component analysis (PCA) are used in many application areas.
The application of electronic computers to factor analysis
H. F. Kaiser · 1960
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A rationale and test for the number of factors in factor analysis
J. L. Horn · 1965
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The scree test for the number of factors
R. B. Cattell · 1966
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Distribution of eigenvalues for some sets of random matrices
V. A. Marchenko and L. A. Pastur · 1967
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Algorithms for Minimization without Derivatives
R. P. Brent · 1973
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Comparison of five rules for determining the number of components to retain
W. R. Zwick and W. F. Velicer · 1986
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Remarks on parallel analysis
A. Buja and N. Eyuboglu · 1992
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An improvement on Horn’s parallel analysis methodology for selecting the correct number of factors to retain
L. W. Glorfeld · 1995
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Analysis of the limiting spectral distribution of large dimensional random matrices
J. W. Silverstein and S.-I. Choi · 1995
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On the distribution of the largest eigenvalue in principal components analysis
I. M. Johnstone · 2001
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A human genome diversity cell line panel
H. M. Cann, C. De Toma, L. Cazes, M.-F. Legrand, V. Morel, L. Piouffre, J. Bodmer, W. F. Bodmer, B. Bonne-Tamir, A. Cambon-Thomsen, et al · 2002
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Factor analysis in chemistry
E. R. Malinowski · 2002
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Phase transition of the largest eigenvalue for nonnull complex sample covariance matrices
J. Baik, G. Ben Arous, and S. Péché · 2005
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Population structure and eigenanalysis
N. Patterson, A. Price, and D. Reich · 2006
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On asymptotics of eigenvectors of large sample covariance matrix
Z. Bai, B. Miao, and G. Pan · 2007
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Capturing heterogeneity in gene expression studies by surrogate variable analysis
J. T. Leek and J. D. Storey · 2007
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Large dimensional factor analysis
J. Bai and S. Ng · 2008
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A general framework for multiple testing dependence
J. T. Leek and J. D. Storey · 2008
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Worldwide human relationships inferred from genome-wide patterns of variation
J. Z. Li, D. M. Absher, H. Tang, A. M. Southwick, A. M. Casto, S. Ramachandran, H. M. Cann, G. S. Barsh, M. Feldman, L. L. Cavalli-Sforza, and R. M. Myers · 2008
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Sample eigenvalue based detection of high-dimensional signals in white noise using relatively few samples
R. R. Nadakuditi and A. Edelman · 2008
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Cross-validation for unsupervised learning
P. O. Perry · 2009
The optimal hard threshold for singular values is 4/sqrt(3)
M. Gavish and D. L. Donoho · 2014
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Optshrink: An algorithm for improved low-rank signal matrix denoising by optimal, data-driven singular value shrinkage
R. R. Nadakuditi · 2014
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Random matrix theory in statistics: A review
D. Paul and A. Aue · 2014
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Efficient computation of limit spectra of sample covariance matrices
E. Dobriban · 2015
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Large Sample Covariance Matrices and High-Dimensional Data Analysis
J. Yao, Z. Bai, and S. Zheng · 2015
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Spectral analysis of large dimensional random matrices
Z. Bai and J. W. Silverstein · 2010
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A rotation test to verify latent structure
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Statistical analysis of factor models of high dimension
J. Bai and K. Li · 2012
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The singular values and vectors of low rank perturbations of large rectangular random matrices
F. Benaych-Georges and R. R. Nadakuditi · 2012
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Inference of population splits and mixtures from genome-wide allele frequency data
J. K. Pickrell and J. K. Pritchard · 2012
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Multiple hypothesis testing adjusted for latent variables, with an application to the AGEMAP gene expression data
Y. Sun, N. R. Zhang, and A. B. Owen · 2012
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E. Dobriban, W. Leeb, and A. Singer · 2016
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Large complex correlated Wishart matrices: Fluctuations and asymptotic independence at the edges
W. Hachem, A. Hardy, and J. Najim · 2016
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Tracy–Widom distribution for the largest eigenvalue of real sample covariance matrices with general population
J. O. Lee and K. Schnelli · 2016
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Bi-cross-validation for factor analysis
A. B. Owen and J. Wang · 2016
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Factor selection by permutation
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