.. _example_covariance_plot_lw_vs_oas.py: ============================= Ledoit-Wolf vs OAS estimation ============================= 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:** 6.24 seconds ( 0 minutes 6.24 seconds)