mlsanityData to 14 Oct 2026

WeSpeR: Computing non-linear shrinkage formulas for the weighted sample covariance

arXiv 2410.14413 ↗First posted 18 Oct 2024math.ST cs.LG math.PRcited by 0

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Abstract (arXiv)

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 textitWeSpeR algorithm and significantly speed up non-linear shrinkage in dimension higher than 1000. Empirical tests confirm the good properties of the textitWeSpeR algorithm. We provide the implementation in PyTorch for it.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 23 references that are in the map. First posted on arXiv 18 Oct 2024.

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References · 2

  1. 2021Analysis of stochastic Lanczos quadrature for spectrum approximationcited by 17
  2. 2020Estimation of Large Financial Covariances: A Cross-Validation Approachcited by 4

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