# Analysis of a multi-target linear shrinkage covariance estimator

arXiv: https://arxiv.org/abs/2405.20086 · HTML: https://mlsanity.com/paper/2405.20086/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 25 references that are in the map.
- First posted on arXiv 30 May 2024.

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

- [Shrinkage estimation of large covariance matrices using multiple shrinkage targets](https://mlsanity.com/paper/1809.08024/) (2018)
- [Ledoit-Wolf linear shrinkage with unknown mean](https://mlsanity.com/paper/2304.07045/) (2023)

## Abstract (arXiv)

Multi-target linear shrinkage is an extension of the standard single-target linear shrinkage for covariance estimation. We combine several constant matrices - the targets - with the sample covariance matrix. We derive the oracle and a textitbona fide multi-target linear shrinkage estimator with exact and empirical mean. In both settings, we proved its convergence towards the oracle under Kolmogorov asymptotics. Finally, we show empirically that it outperforms other standard estimators in various situations.