Efficient approximate k-fold and leave-one-out cross-validation for ridge regression In model building and model evaluation, cross-validation is a frequently used resampling method. Unfortunately, this method can be quite time consuming. In this article, we discuss an approximation method that is much faster and can be used in generalized linear models and Cox’ proportional hazards model with a ridge penalty term. Our approximation method is based on a Taylor expansion around the estimate of the full model. In this way, all cross-validated estimates are approximated without refitting the model. The tuning parameter can now be chosen based on these approximations and can be optimized in less time. The method is most accurate when approximating leave-one-out cross-validation results for large data sets which is originally the most computationally demanding situation. In order to demonstrate the method’s performance, it will be applied to several microarray data sets. An R package penalized, which implements the method, is available on CRAN. (Source:

References in zbMATH (referenced in 14 articles )

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  1. Groll, Andreas; Tutz, Gerhard: Variable selection in discrete survival models including heterogeneity (2017)
  2. Michael J. Wurm, Paul J. Rathouz, Bret M. Hanlon: Regularized Ordinal Regression and the ordinalNet R Package (2017) arXiv
  3. Moore, Dirk F.: Applied survival analysis using R (2016)
  4. Tutz, Gerhard; Koch, Dominik: Improved nearest neighbor classifiers by weighting and selection of predictors (2016)
  5. Tutz, Gerhard; Schmid, Matthias: Modeling discrete time-to-event data (2016)
  6. Scutari, Marco; Denis, Jean-Baptiste: Bayesian networks. With examples in R (2015)
  7. Chaturvedi, Nimisha; De Menezes, Renée X.; Goeman, Jelle J.: Fused lasso algorithm for $\mathrmCox^\prime$ proportional hazards and binomial logit models with application to copy number profiles (2014)
  8. Meijer, Rosa J.; Goeman, Jelle J.: Efficient approximate $k$-fold and leave-one-out cross-validation for ridge regression (2013)
  9. Nagarajan, Radhakrishnan; Scutari, Marco; Lèbre, Sophie: Bayesian networks in R. With applications in systems biology (2013)
  10. Scutari, Marco; Mackay, Ian; Balding, David: Improving the efficiency of genomic selection (2013)
  11. Yang, Yi; Zou, Hui: A cocktail algorithm for solving the elastic net penalized Cox’s regression in high dimensions (2013)
  12. Perperoglou, Aris: Fitting survival data with penalized Poisson regression (2011)
  13. Benner, Axel; Zucknick, Manuela; Hielscher, Thomas; Ittrich, Carina; Mansmann, Ulrich: High-dimensional Cox models: The choice of penalty as part of the model building process (2010)
  14. Goeman, Jelle J.: $L_1$ penalized estimation in the Cox proportional hazards model (2010)