.. _example_covariance_plot_lw_vs_oas.py:
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Ledoit-Wolf vs OAS estimation
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The usual covariance maximum likelihood estimate can be regularized
using shrinkage. Ledoit and Wolf proposed a close formula to compute
the asymptotically optimal shrinkage parameter (minimizing a MSE
criterion), yielding the Ledoit-Wolf covariance estimate.
Chen et al. proposed an improvement of the Ledoit-Wolf shrinkage
parameter, the OAS coefficient, whose convergence is significantly
better under the assumption that the data are Gaussian.
This example, inspired from Chen's publication [1], shows a comparison
of the estimated MSE of the LW and OAS methods, using Gaussian
distributed data.
[1] "Shrinkage Algorithms for MMSE Covariance Estimation"
Chen et al., IEEE Trans. on Sign. Proc., Volume 58, Issue 10, October 2010.
.. image:: images/plot_lw_vs_oas_001.png
:align: center
**Python source code:** :download:`plot_lw_vs_oas.py `
.. literalinclude:: plot_lw_vs_oas.py
:lines: 23-
**Total running time of the example:** 5.12 seconds
( 0 minutes 5.12 seconds)