%0 Journal Article %1 Lehmann200551 %A Lehmann, Nils %D 2005 %J Statistics & Probability Letters %K PCA random_matrices %N 1 %P 51 - 58 %R http://dx.doi.org/10.1016/j.spl.2005.04.031 %T Principal components selection given extensively many variables %U http://www.sciencedirect.com/science/article/pii/S0167715205001574 %V 74 %X Principal components analysis relates to the eigenvalue distribution of Wishart matrices. Given few observations and very many variables this distribution maps to eigenvalue statistics in the Gaussian orthogonal ensemble. Principal components selection can then be based on existing analytical results.

@article{Lehmann200551, abstract = {Principal components analysis relates to the eigenvalue distribution of Wishart matrices. Given few observations and very many variables this distribution maps to eigenvalue statistics in the Gaussian orthogonal ensemble. Principal components selection can then be based on existing analytical results.}, added-at = {2014-04-03T20:36:20.000+0200}, author = {Lehmann, Nils}, biburl = {https://www.bibsonomy.org/bibtex/2d105cb2a88f17e43e177af2f0d08bb79/peter.ralph}, doi = {http://dx.doi.org/10.1016/j.spl.2005.04.031}, interhash = {80048e83a5078760133a9f4d28f335f6}, intrahash = {d105cb2a88f17e43e177af2f0d08bb79}, issn = {0167-7152}, journal = {Statistics & Probability Letters }, keywords = {PCA random_matrices}, number = 1, pages = {51 - 58}, timestamp = {2014-04-03T20:36:20.000+0200}, title = {Principal components selection given extensively many variables }, url = {http://www.sciencedirect.com/science/article/pii/S0167715205001574}, volume = 74, year = 2005 }