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BMC Medical Research Methodology|December 23, 2009
Optimal classifier selection and negative bias in error rate estimation: an empirical study on high-dimensional predictionAnne-Laure Boulesteix, Carolin StroblBMC Bioinformatics|April 9, 2013
An AUC-based permutation variable importance measure for random forestsSilke Janitza, Carolin Strobl, Anne-Laure BoulesteixBMC Bioinformatics|January 27, 2007
Bias in random forest variable importance measures: illustrations, sources and a solutionCarolin Strobl, Anne-Laure Boulesteix, Achim Zeileis, et al.Briefings in Bioinformatics|September 13, 2011
Random forest Gini importance favours SNPs with large minor allele frequency: impact, sources and recommendationsAnne-Laure Boulesteix, Andreas Bender, Justo Lorenzo Bermejo, et al.BMC Bioinformatics|July 16, 2008
Conditional variable importance for random forestsCarolin Strobl, Anne-Laure Boulesteix, Thomas Kneib, et al.Briefings in Bioinformatics|April 12, 2014
Letter to the Editor: On the term 'interaction' and related phrases in the literature on Random ForestsAnne-Laure Boulesteix, Silke Janitza, Alexander Hapfelmeier, et al.Statistical Applications in Genetics and Molecular Biology|January 4, 2008
Multiple testing for SNP-SNP interactionsAnne-Laure Boulesteix, Carolin Strobl, Stefan Weidinger, et al.Bioinformatics (Oxford, England)|May 15, 2007
WilcoxCV: an R package for fast variable selection in cross-validationAnne-Laure BoulesteixBiometrical Journal. Biometrische Zeitschrift|July 19, 2006
Maximally selected chi-square statistics for ordinal variablesAnne-Laure BoulesteixStatistical Applications in Genetics and Molecular Biology|October 20, 2006
Reader's reaction to "Dimension reduction for classification with gene expression microarray data" by Dai et al (2006)Anne-Laure BoulesteixPageof 13