Tuning-free ridge estimators for high-dimensional generalized linear models

Ridge estimators regularize the squared Euclidean lengths of parameters. Such are mathematically and computationally attractive but involve tuning parameters that need to be calibrated. It is shown ridge can modified such avoided altogether, resulting estimator improve on prediction accuracies standard combined with cross-validation.