MSE Comparison
Below are results obtained with a variety of data matrices of dimensions $n\times p$. For each pair of dimension, 50 covariance matrices are generated with associated sample data matrices. The covariance obtained with the different estimators are then compared to the ground-truth and the MSE is reported.
| Abbreviation | Method |
|---|---|
anshrink | analytical nonlinear shrinkage |
ccor | LSE with constant correlation target |
ccov | LSE with constant covariance target |
d1v | LSE with identity target |
dcv | LSE with diagonal common variance target |
duv | LSE with diagonal unequal variance target |
ppc | LSE with perfect positive correlation target |
s | Simple estimator (baseline) |
_lw | uses ledoit-wolf shrinkage |
_ss | uses schaffer-strimmer shrinkage |
_oas | uses oracle approximating shrinkage |
_rblw | uses rao-blackwellised ledoit-wolf shrinkage |
Fat matrices
Tall matrices
