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

arXiv: https://arxiv.org/abs/2410.14413 · HTML: https://mlsanity.com/paper/2410.14413/

## 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.

## Builds on (most cited first, 2 of 2)

- [Analysis of stochastic Lanczos quadrature for spectrum approximation](https://mlsanity.com/paper/2105.06595/) (2021)
- [Estimation of Large Financial Covariances: A Cross-Validation Approach](https://mlsanity.com/paper/2012.05757/) (2020)

## 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.