STABLE ESTIMATORS OF INVERSE COVARIANCE MATRICES

摘要

The problem is posed of best regularized estimator choice not going beyond the class of all possible ridge estimators and their random linear combinations. The estimators are obtained for unknown inverse covariance matrices with minimal limit quadratic risk. These estimators are proved to ensure minimal limit quadratic risk regardless of distributions.