Geometric representation of high dimension, low sample size data

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Hall, Peter
Marron, J S
Neeman, Amnon

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Aiden Press

Abstract

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 s

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Journal of the Royal Statistical Society Series B

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Restricted until

2037-12-31