Nonparametric eigenvalue-regularized precision or covariance matrix estimator

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High‐dimensional covariance matrix estimation (2019, methodology): “We compare 5 estimators: Graphical lasso (GLASSO) from Section 2.3, the NOVELIST estimator by Huang and Fryzlewicz (2018), NERCOME by Lam (2016), nonlinear shrinkage (Nonlin) estimator by Ledoit and Wolf (2012), and (cid:12)nally the grand average (Grand Avg.) estimator by Abadir et al. (2014).”
The Power of (Non-)Linear Shrinking: A Review and Guide to Covariance Matrix Estimation (2020, unknown): “The title of our paper (Ledoit and Wolf, 2020) stems from the fact that the outlined method is the first to “directly” estimate the oracle shrinkage function / with the analytical formula (3.13). In the earlier works (Ledoit and Wolf, 2012, 2015), we had proposed alternative, “indirect” estimation s”
Quadratic Shrinkage for Large Covariance Matrices (2020, methodology): “But unreported Monte Carlo simulations show that, just like the QuEST method and the NERCOME method of Lam (2016), which are both of numerical nature, quadratic-inverse shrinkage also can successfully handle this case in practice.”