Shrinkage and Penalized Likelihood as Methods to Improve Predictive Accuracy

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Minimum sample size for developing a multivariable prediction model: PART II ‐ binary and time‐to‐event outcomes (2018, methodology): “69, and so we can use equation (22) to predict the corresponding D statistic:”
Cross-Validation, Shrinkage and Variable Selection in Linear Regression Revisited (2013, background): “However, the results show actual val f the global ge factor ret [9] hrinkage is a bit counter9.”
MMI: Multimodel inference or models with management implications? (2015, methodology): “A variety of other shrinkage methods (e.g., penalized likelihood, ridge regression, LASSO) have been developed to improve model performance (e.g., reviews by van Houwelingen 2001, Hooten and Hobbs 2015).”
Variable selection – A review and recommendations for the practicing statistician (2018, methodology): “…to point at the shrinkage methods that were developed to reduce the MSE of estimates and predictions, and which can also serve to reduce a possible overestimation bias induced by variable selection (Dunkler et al., 2016; Sauerbrei, 1999; van Houwelingen, 2001; van Houwelingen & Sauerbrei, 2013).”