Tuning Parameter Selection

The restricted consistency property of leave-$n_v$-out cross-validation for high-dimensional variable selection

Cross-validation (CV) methods are popular for selecting the tuning parameter in the high-dimensional variable selection problem. We show the mis-alignment of the CV is one possible reason of its over-selection behavior. To fix this issue, we …

Tuning-parameter selection in regularized estimations of large covariance matrices

Recently many regularized estimators of large covariance matrices have been proposed, and the tuning parameters in these estimators are usually selected via cross-validation. However, there is a lack of consensus on the number of folds for conducting …

Modified Cross-Validation for LASSO Penalized High-Dimensional Linear Models

In this article, for Lasso penalized linear regression models in high-dimensional settings, we propose a modified cross-validation (CV) method for selecting the penalty parameter. The methodology is extended to other penalties, such as Elastic Net. …