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Biotechnology and Bioengineering|August 18, 2016
Boosted structured additive regression for Escherichia coli fed-batch fermentation modelingMichael Melcher, Theresa Scharl, Markus Luchner, et al.
BMC Bioinformatics|January 27, 2007
Bias in random forest variable importance measures: illustrations, sources and a solutionCarolin Strobl, Anne-Laure Boulesteix, Achim Zeileis, et al.
The British Journal of Mathematical and Statistical Psychology|June 7, 2022
Score-based measurement invariance checks for Bayesian maximum-a-posteriori estimates in item response theoryRudolf Debelak, Samuel Pawel, Carolin Strobl, et al.
Psychometrika|November 19, 2017
Score-Based Tests of Differential Item Functioning via Pairwise Maximum Likelihood EstimationTing Wang, Carolin Strobl, Achim Zeileis, et al.
Pediatric Diabetes|February 19, 2016
Associations of growth patterns and islet autoimmunity in children with increased risk for type 1 diabetes: a functional analysis approachChristina Yassouridis, Friedrich Leisch, Christiane Winkler, 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|March 2, 2010
The behaviour of random forest permutation-based variable importance measures under predictor correlationKristin K Nicodemus, James D Malley, Carolin Strobl, et al.
The Lancet Regional Health. Europe|May 6, 2022
Predictors of depression among middle-aged and older men and women in Europe: A machine learning approachElizabeth P Handing, Carolin Strobl, Yuqin Jiao, et al.
Applied Psychological Measurement|April 26, 2021
Anchor Point Selection: Scale Alignment Based on an Inequality CriterionCarolin Strobl, Julia Kopf, Lucas Kohler, et al.
BMC Bioinformatics|July 16, 2008
Conditional variable importance for random forestsCarolin Strobl, Anne-Laure Boulesteix, Thomas Kneib, et al.
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