Carolin Strobl

发表

Detecting Differential Item and Step Functioning with Rating Scale and Partial Credit Trees

Basil Abou El-Komboz, Achim Zeileis, Carolin Strobl, 2014 .

Maximally selected Chi-squared statistics and non-monotonic associations: An exact approach based on two cutpoints

Carolin Strobl, Anne-Laure Boulesteix, A. Boulesteix, 2007, Comput. Stat. Data Anal..

Letter to the Editor: On the term ‘interaction’ and related phrases in the literature on Random Forests

Carolin Strobl, Anne-Laure Boulesteix, Silke Janitza, 2014, Briefings Bioinform..

Conditional permutation importance revisited

Carolin Strobl, Dries Debeer, 2020, BMC Bioinformatics.

Random forest Gini importance favours SNPs with large minor allele frequency: impact, sources and recommendations

Carolin Strobl, Andreas Bender, Anne-Laure Boulesteix, 2012, Briefings Bioinform..

A Comparison of Aggregation Rules for Selecting Anchor Items in Multigroup DIF Analysis

Carolin Strobl, Thorben Huelmann, Rudolf Debelak, 2020, Journal of Educational Measurement.

Variable Selection in Classification Trees Based on Imprecise Probabilities

Carolin Strobl, C. Strobl, 2005, ISIPTA.

BMC Bioinformatics BioMed Central Methodology article Conditional variable importance for random forests

Achim Zeileis, Carolin Strobl, Anne-Laure Boulesteix, 2008 .

A New, Conditional Variable-Importance Measure for Random Forests Available in the party Package

Achim Zeileis, Carolin Strobl, Torsten Hothorn, 2009 .

A new variable importance measure for random forests with missing data

Carolin Strobl, Torsten Hothorn, Alexander Hapfelmeier, 2012, Statistics and Computing.

Unbiased split selection for classification trees based on the Gini Index

Carolin Strobl, Anne-Laure Boulesteix, Thomas Augustin, 2007, Comput. Stat. Data Anal..

The potential of model-based recursive partitioning in the social sciences - Revisiting Ockham's Razor

Carolin Strobl, Thomas Augustin, Julia Kopf, 2010 .

The behaviour of random forest permutation-based variable importance measures under predictor correlation

Carolin Strobl, James D. Malley, Andreas Ziegler, 2010, BMC Bioinformatics.

Rasch Trees: A New Method for Detecting Differential Item Functioning in the Rasch Model

Achim Zeileis, Carolin Strobl, Julia Kopf, 2015, Psychometrika.

Random Forests with Missing Values in the Covariates

Carolin Strobl, Torsten Hothorn, Anna K Rieger, 2010 .

Measuring the Stability of Results From Supervised Statistical Learning

Kurt Hornik, Carolin Strobl, Michel Philipp, 2018, Journal of Computational and Graphical Statistics.

(Psycho-)analysis of benchmark experiments: A formal framework for investigating the relationship between data sets and learning algorithms

Carolin Strobl, Manuel J. A. Eugster, Friedrich Leisch, 2014, Comput. Stat. Data Anal..

Bias in random forest variable importance measures: Illustrations, sources and a solution

Achim Zeileis, Carolin Strobl, Torsten Hothorn, 2007, BMC Bioinformatics.

Classification - An Overview over Standard and Credal Classification Approaches with an Emphasis on Instability and Interpretability

Carolin Strobl, 2008 .

Statistical issues in machine learning: towards reliable split selection and variable importance measures

Carolin Strobl, C. Strobl, 2008 .

Measuring the Stability of Recursive Partitioning Methods

Kurt Hornik, Carolin Strobl, Michel Philipp, 2018 .

Investigating Measurement Invariance by Means of Parameter Instability Tests for 2PL and 3PL Models

Carolin Strobl, Rudolf Debelak, 2019, Educational and psychological measurement.

Flexible Rasch Mixture Models with Package psychomix

Achim Zeileis, Carolin Strobl, Friedrich Leisch, 2012 .

An AUC-based permutation variable importance measure for random forests

Carolin Strobl, Anne-Laure Boulesteix, Silke Janitza, 2013, BMC Bioinformatics.

Anchor Selection Strategies for DIF Analysis

Achim Zeileis, Carolin Strobl, Julia Kopf, 2015, Educational and psychological measurement.

A Laboratory for Recursive Partytioning [R package party version 1.3-5]

Kurt Hornik, Achim Zeileis, Carolin Strobl, 2020 .

Score-Based Tests of Differential Item Functioning in the Two-Parameter Model

Achim Zeileis, Carolin Strobl, Ting Wang, 2016 .

Why and how to use random forest variable importance measures (and how you shouldn't)

Achim Zeileis, Carolin Strobl, 2008 .

Accounting for Individual Differences in Bradley-Terry Models by Means of Recursive Partitioning

Achim Zeileis, Carolin Strobl, Florian Wickelmaier, 2011 .

Statistical sources of variable selection bias in classification tree algorithms based on the Gini index

Carolin Strobl, C. Strobl, 2005 .

Statistical Applications in Genetics and Molecular Biology Multiple Testing for SNP-SNP Interactions

Carolin Strobl, Anne-Laure Boulesteix, Stefan Wagenpfeil, 2007 .

Adaptive Selection of Extra Cutpoints — Towards Reconciling Robustness and Interpretability in Classification Trees

Carolin Strobl, Thomas Augustin, 2009 .

On the Estimation of Standard Errors in Cognitive Diagnosis Models

Achim Zeileis, Carolin Strobl, Michel Philipp, 2018 .

Variable Selection Bias in Classification Trees Based on Imprecise Probabilities

Carolin Strobl, C. Strobl, 2005 .

Tree-Based Global Model Tests for Polytomous Rasch Models

Achim Zeileis, Carolin Strobl, Basil Komboz, 2018, Educational and psychological measurement.

A new method for detecting differential item functioning in the Rasch model

Achim Zeileis, Carolin Strobl, Julia Kopf, 2011 .