Innocent Nsabimana

Spectral Shrinkage in High-Dimensional Statistics: From Random Matrix Theory to Optimal Covariance Estimation

In the era of high-dimensional data, the classical assumption that the number of observations n vastly exceeds the number of variables p is frequently violated. When p and n grow proportionally (p/n → c > 0), the sample covariance matrix becomes severely distorted by sampling noise. Its eigenvalues are systematically b...