Robust Shrinkage Estimation of High-dimensional Covariance Matrices

Details Robust Shrinkage Estimation of High-dimensional Covariance Matrices Robust Shrinkage Estimation of High-dimensional Covariance Matrices

2010-09-27

arxiv.org/abs/1009.5331v1

Statistics

Methodology

Scientific paper

We address high dimensional covariance estimation for elliptical distributed samples, which are also known as spherically invariant random vectors (SIRV) or compound-Gaussian processes. Specifically we consider shrinkage methods that are suitable for high dimensional problems with a small number of samples (large $p$ small $n$). We start from a classical robust covariance estimator [Tyler(1987)], which is distribution-free within the family of elliptical distribution but inapplicable when $n

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