Sancetta, Alessio - In: Journal of Multivariate Analysis 99 (2008) 5, pp. 949-967
For high dimensional data sets the sample covariance matrix is usually unbiased but noisy if the sample is not large enough. Shrinking the sample covariance towards a constrained, low dimensional estimator can be used to mitigate the sample variability. By doing so, we introduce bias, but reduce...