Abstract
Provides nonparametric Steinian shrinkage estimators of the covariance matrix that are suitable in high dimensional settings, that is when the number of variables is larger than the sample size.
Original language | English |
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Place of Publication | CRAN |
Media of output | Online |
Publication status | Accepted/In press - 17 Apr 2014 |
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Anestis Touloumis
- School of Arch, Tech and Eng - Principal Lecturer
- Computing and Mathematical Sciences Research Excellence Group
Person: Academic