DOC Correction in empirical covariance
@@ -27,8 +27,8 @@ by the classical *maximum likelihood estimator* (or "empirical
covariance"), provided the number of observations is large enough
compared to the number of features (the variables describing the
observations). More precisely, the Maximum Likelihood Estimator of a
-sample is an unbiased estimator of the corresponding population's
-covariance matrix.
+sample is an asymptotically unbiased estimator of the corresponding
+population's covariance matrix.
The empirical covariance matrix of a sample can be computed using the
:func:`empirical_covariance` function of the package, or by fitting an
@@ -55,8 +55,8 @@ Shrunk Covariance
Basic shrinkage
---------------
-Despite being an unbiased estimator of the covariance matrix, the
-Maximum Likelihood Estimator is not a good estimator of the
+Despite being an asymptotically unbiased estimator of the covariance matrix,
+the Maximum Likelihood Estimator is not a good estimator of the
eigenvalues of the covariance matrix, so the precision matrix obtained
from its inversion is not accurate. Sometimes, it even occurs that the
empirical covariance matrix cannot be inverted for numerical
