• Bernau, C., Augustin, T. and Boulesteix, A. L. (2013). Correcting the optimal resampling-based error rate by estimating the error rate of wrapper algorithms. Biometrics 69, 693702.
  • Boulesteix, A.-L., Janitza, S., Kruppa, J. and König, I. R. (2012). Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2, 493507.
  • Boulesteix, A.-L., Richter, A. and Bernau, C. (2013). Complexity selection with cross-validation for lasso and sparse partial least squares using high-dimensional data. In: Algorithms from and for Nature and Life. Springer, Berlin, DE, pp. 261268.
  • Boulesteix, A. L. and Strobl, C. (2009). Optimal classifier selection and negative bias in error rate estimation: an empirical study on high-dimensional prediction. BMC Medical Research Methodology 9, 85.
  • Breiman, L. (2001). Statistical modeling: The two cultures (with comments and a rejoinder by the author). Statistical Science 16, 199231.
  • Efron, B. and Tibshirani, R. (1997). Improvements on cross-validation: the .632+ bootstrap method. Journal of the American Statistical Association 92, 548560.
  • Hothorn, T., Bühlmann, P., Kneib, T., Schmid, M. and Hofner, B. (2010). Model-based boosting 2.0. Journal of Machine Learning Research 11, 21092113.
  • Hothorn, T., Held, L. and Friede, T. (2009). Biometrical journal and reproducible research. Biometrical Journal 51, 553555.
  • Jelizarow, M., Guillemot, V., Tenenhaus, A., Strimmer, K. and Boulesteix, A.-L. (2010). Over-optimism in bioinformatics: an illustration. Bioinformatics 26, 19901998.
  • Kruppa, J., Liu, Y., Biau, G., Kohler, M., König, I. R., Malley, J. D. and Ziegler, A. (2014a). Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory. Biometrical Journal 56, 534563.
  • Kruppa, J., Liu, Y., Diener, H.-C., Holste, T., Weimar, C., König, I. R. and Ziegler, A. (2014b). Probability estimation with machine learning methods for dichotomous and multicategory outcome: Applications. Biometrical Journal 56, 564583.
  • Molinaro, A. M., Simon, R. and Pfeiffer, R. M. (2005). Prediction error estimation: a comparison of resampling methods. Bioinformatics 21, 33013307.
  • Schmid, M., Kestler, H. A. and Potapov, S. (2013). On the validity of time-dependent AUC estimators. Briefings in Bioinformatics, doi:10.1093/bib/bbt059.
  • Schwarz, D. F., König, I. R. and Ziegler, A. (2010). On safari to random jungle: a fast implementation of random forests for high-dimensional data. Bioinformatics 26, 17521758.
  • Shmueli, G. (2010). To explain or to predict? Statistical Science 25, 289310.
  • Simmons, J. P., Nelson, L. D. and Simonsohn, U. (2011). False-positive psychology undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science 22, 13591366.
  • To, M., Skentou, C., Royston, P., Yu, C. and Nicolaides, K. (2006). Prediction of patient-specific risk of early preterm delivery using maternal history and sonographic measurement of cervical length: a population-based prospective study. Ultrasound in Obstetrics & Gynecology 27, 362367.
  • Wolpert, D. (2001). The supervised learning no-free-lunch theorems. In Proceedings of the 6th Online World Conference on Soft Computing in Industrial Applications. pp. 1024.