The tuning parameter selection strategy for penalized estimation is crucial to identify a model that both interpretable and predictive. However, popular strategies (e.g., minimizing average squared prediction error via cross-validation) tend select models with more predictors than necessary. A simple yet powerful cross validation proposed which based on maximizing the correlation between observ...
