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Wiley InterScience | ||||||||||||||
  Journal of the Royal Statistical Society: Series B (Statistical Methodology)Volume 67 Issue 3, Pages 427 - 444 Published Online: 24 May 2005 © 2010 The Royal Statistical Society and Blackwell Publishing Ltd Published on behalf of the Royal Statistical Society
Abstract | References | Full Text: HTML, PDF (Size: 245K) | Related Articles | Citation Tracking  Geometric representation of high dimension, low sample size data Copyright 2005 Royal Statistical Society KEYWORDS Chemometrics • Large dimensional data • Medical images • Microarrays • Multivariate analysis • Non-standard asymptotics ABSTRACTSummary. High dimension, low sample size data are emerging in various areas of science. We find a common structure underlying many such data sets by using a non-standard type of asymptotics: the dimension tends to ∞ while the sample size is fixed. Our analysis shows a tendency for the data to lie deterministically at the vertices of a regular simplex. Essentially all the randomness in the data appears only as a random rotation of this simplex. This geometric representation is used to obtain several new statistical insights. [Received April 2004. Revised February 2005] | 
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 | Significance |
Try to forecast the results of 10 different events, some sporting, some cultural, some just odd, that will take place between May and July 2010. Have Fun! | |
