Optimal shrinkage for robust covariance matrix estimators in a small sample size setting
Optimal shrinkage for robust covariance matrix estimators in a small sample size setting
When estimating covariance matrices, traditional sample covariance-based estimators are straightforward but suffer from two main issues: 1) a lack of robustness, which occurs as soon as the samples do not come from a Gaussian distribution or are contaminated with outliers and 2) a lack of data, which occurs as soon …