Regularization approaches in clinical biostatistics: A review of methods and their applications

Sarah Friedrich, Andreas Groll, Katja Ickstadt, Thomas Kneib, Markus Pauly, Jörg Rahnenführer & Tim Friede
A range of regularization approaches have been proposed in the data sciences to overcome overfitting, to exploit sparsity or to improve prediction. Using a broad definition of regularization, namely controlling model complexity by adding information in order to solve ill-posed problems or to prevent overfitting, we review a range of approaches within this framework including penalization, early stopping, ensembling and model averaging. Aspects of their practical implementation are discussed including available R-packages and examples are...
This data repository is not currently reporting usage information. For information on how your repository can submit usage information, please see our documentation.