[1] A. Bhattacharjee, W. Richards, J. Staunton, C. Li, S. Monti, P. Vasa, C. Ladd, J. Beheshti, R. Bueno, M. Gillette, M. Loda, G. Weber, E. J. Mark, E. S. Lander, W. Wong, B. E. Johnson, T. R. Golub, D. J. Sugarbaker, M. Meyerson, Classification of human lung carcinomas by mrna expression profiling reveals distinct adenocarcinoma subclasses, Proc. Natl. Acad. Sci. USA 98(24):13790-5.

[2] J. Ahn, J.S. Marron, K.M. Muller, Y.-Y. Chi  The high-dimension, low-sample-size geometric representation holds under mild conditions Biometrika, 94 (3) (2007), pp. 760–766

[3] Z. Bai, J.W. Silverstein Spectral Analysis of Large Dimensional Random Matrices, Springer Series in Statistics (second ed.), Springer, New York (2010) http://dx.doi.org/10.1007/978-1-4419-0661-8

[4] J. Baik, J.W. Silverstein Eigenvalues of large sample covariance matrices of spiked population models J. Multivariate Anal., 97 (6) (2006), pp. 1382–1408

[5] A. Bhattacharjee, W. Richards, J. Staunton, C. Li, S. Monti, P. Vasa, C. Ladd, J. Beheshti, R. Bueno, M.  Gillette, M. Loda, G. Weber, E.J. Mark, E.S. Lander, W. Wong, B.E. Johnson, T.R. Golub, D.J. Sugarbaker, M. Meyerson Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses Proc. Natl. Acad. Sci. USA, 98 (24) (2001), pp. 13790–13795

[6] R.C. Bradley Basic properties of strong mixing conditions. A survey and some open questions Probab. Surv., 2 (2005), pp. 107–144 (electronic), Update of, and a supplement to, the 1986 original

[7] G. Casella, J.T. Hwang Limit expressions for the risk of James–Stein estimators Canad. J. Statist., 10 (4) (1982), pp. 305–309 http://dx.doi.org/10.2307/3556196

[8] N. El Karoui Spectrum estimation for large dimensional covariance matrices using random matrix theory Ann. Statist., 36 (6) (2008), pp. 2757–2790 http://dx.doi.org/10.1214/07-AOS581

[9] T.L. Gaydos, Data representation and basis selection to understand variation of function valued traits, Ph.D. Thesis, University of North Carolina at Chapel Hill, 2008.

[10] G.H. Golub, C.F. Van Loan Matrix Computations, Johns Hopkins Studies in the Mathematical Sciences (third ed.), Johns Hopkins University Press, Baltimore, MD (1996)

[11] P. Hall, J.S. Marron, A. Neeman Geometric representation of high dimension, low sample size data J. R. Stat. Soc. Ser. B Stat. Methodol., 67 (3) (2005), pp. 427–444

[12] H. Huang, Y. Liu, J.S. Marron, Bi-directional discrimination with application to data visualization, manuscript, 2012.

[13] I.M. Johnstone On the distribution of the largest eigenvalue in principal components analysis Ann. Statist., 29 (2) (2001), pp. 295–327

[14] S. Jung, J.S. Marron PCA consistency in high dimension, low sample size context Ann. Statist., 37 (6B) (2009), pp. 4104–4130

[15] A.N. Kolmogorov, Y.A. Rozanov On strong mixing conditions for stationary Gaussian processes Theory Probab. Appl., 5 (2) (1960), pp. 204–208

[16] S. Lee, F. Zou, F.A. Wright Convergence and prediction of principal component scores in high-dimensional settings Ann. Statist., 38 (6) (2010), pp. 3605–3629 http://dx.doi.org/10.1214/10-AOS821

[17] R.J. Muirhead Aspects of Multivariate Statistical Theory, Wiley Series in Probability and Mathematical Statistics, John Wiley & Sons Inc., New York (1982)

[18] B. Nadler Finite sample approximation results for principal component analysis: a matrix perturbation approach Ann. Statist., 36 (6) (2008), pp. 2791–2817 http://dx.doi.org/10.1214/08-AOS618

[19] D. Paul Asymptotics of sample eigenstructure for a large dimensional spiked covariance model Statist. Sinica, 17 (2007), pp. 1617–1642

[20] F. Pesarin, L. Salmaso Finite-sample consistency of combination-based permutation tests with application to repeated measures designs J. Nonparametr. Stat., 22 (5–6) (2010), pp. 669–684 http://dx.doi.org/10.1080/10485250902807407

[21] F. Pesarin, L. Salmaso Permutation Tests for Complex Data: Theory, Applications and Software Wiley, Chichester, UK (2010)

[22] X. Qiao, H.H. Zhang, Y. Liu, M. Todd, J.S. Marron Weighted distance weighted discrimination and its asymptotic properties J. Amer. Statist. Assoc., 105 (489) (2010), pp. 401–414

[23] G.W. Stewart, J.G. Sun Matrix Perturbation Theory, Computer Science and Scientific Computing, Academic Press Inc., Boston, MA (1990)

[24] K. Yata, M. Aoshima Effective PCA for high-dimension, low-sample-size data with singular value decomposition of cross data matrix J. Multivariate Anal., 101 (9) (2010), pp. 2060–2077 http://dx.doi.org/10.1016/j.jmva.2010.04.006

[25] K. Yata, M. Aoshima PCA consistency for non-Gaussian data in high dimension, low sample size context Comm. Statist. Theory Methods, 38 (16–17) (2009), pp. 2634–2652