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📄 Abstract
Abstract: We address the issue of computing the non-linear shrinkage formulas for the
weighted sample covariance in high dimension. We use theoretical properties of
the asymptotic sample spectrum in order to derive the \textit{WeSpeR} algorithm
and significantly speed up non-linear shrinkage in dimension higher than
$1000$. Empirical tests confirm the good properties of the \textit{WeSpeR}
algorithm. We provide the implementation in PyTorch for it.