Cross-Validated Loss-Based Covariance Matrix Estimator Selection in High Dimensions.

ABSTRACT: The covariance matrix plays a fundamental role in many modern exploratory and inferential statistical procedures, including dimensionality reduction, hypothesis testing, and regression. In low-dimensional regimes, where the number of observations far exceeds the number of variables, the optimality of the sample covariance matrix as an estimator of this parameter is well-established. High-dimensional regimes do not admit such a convenience. Thus, a variety of estimators have been derived to overcome the shortcomings of the canonical estimator in such settings. Yet, selecting an optimal estimator from among the plethora available remains an open challenge. Using the framework of cross-validated loss-based estimation, we develop the theoretical underpinnings of just such an estimator selectio

SUBMITTER: Boileau P 

PROVIDER: S-EPMC10237052 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature