C. Strobl

发表

An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests.

G. Tutz, C. Strobl, J. Malley, 2009, Psychological methods.

A Laboratory for Recursive Partytioning

K. Hornik, T. Hothorn, A. Zeileis, 2015 .

Evaluating Microarray-based Classifiers: An Overview

M. Daumer, A. Boulesteix, C. Strobl, 2008, Cancer informatics.

Danger: High Power! – Exploring the Statistical Properties of a Test for Random Forest Variable Importance

A. Zeileis, C. Strobl, 2008 .

Fitting prediction rule ensembles to psychological research data: An introduction and tutorial.

C. Strobl, M. Fokkema, 2019, Psychological methods.

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

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

Maximally selected chi-square statistics and umbrella orderings

A. Boulesteix, C. Strobl, 2006 .

On the Estimation of Standard Errors in Cognitive Diagnosis Models

Achim Zeileis, Carolin Strobl, Michel Philipp, 2018 .

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

Achim Zeileis, Carolin Strobl, Florian Wickelmaier, 2011 .

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

A. Boulesteix, T. Hothorn, A. Zeileis, 2007, BMC Bioinformatics.

Rasch Mixture Models for DIF Detection

A. Zeileis, C. Strobl, Hannah Frick, 2015, Educational and psychological measurement.

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..

Measuring the Stability of Results From Supervised Statistical Learning

Kurt Hornik, Michel Philipp, Thomas Rusch, 2018, Journal of Computational and Graphical Statistics.

Measuring the Stability of Recursive Partitioning Methods

Kurt Hornik, Carolin Strobl, Michel Philipp, 2018 .

Variable Selection in Classification Trees Based on Imprecise Probabilities

Carolin Strobl, C. Strobl, 2005, ISIPTA.

Torsten Hothorn Bias in Random Forest Variable Importance Measures : Illustrations , Sources and a Solution Paper

A. Boulesteix, T. Hothorn, A. Zeileis, 2016 .

Analysis of the individual and aggregate genetic contributions of previously identified serine peptidase inhibitor Kazal type 5 (SPINK5), kallikrein-related peptidase 7 (KLK7), and filaggrin (FLG) polymorphisms to eczema risk.

C. Strobl, N. Klopp, T. Illig, 2008, The Journal of allergy and clinical immunology.

Analysis of the individual and aggregate genetic contributions of previously identified SPINK 5 , KLK 7 and FLG polymorphisms to eczema risk

C. Strobl, N. Klopp, T. Illig, 2015 .

Predictors of depression among middle-aged and older men and women in Europe: A machine learning approach

Stephen R. Aichele, C. Strobl, L. Feliciano, 2022, The Lancet regional health. Europe.

Against the "one method fits all data sets" philosophy for comparison studies in methodological research.

F. Leisch, C. Strobl, 2022, Biometrical journal. Biometrische Zeitschrift.

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

Kurt Hornik, Achim Zeileis, Carolin Strobl, 2020 .

An R toolbox for score-based measurement invariance tests in IRT models

A. Zeileis, C. Strobl, Rudolf Debelak, 2020, Behavior Research Methods.

A new variable importance measure for random forests with missing data

T. Hothorn, C. Strobl, Alexander Hapfelmeier, 2012, Statistics and Computing.

Score-Based Tests of Differential Item Functioning via Pairwise Maximum Likelihood Estimation

A. Zeileis, C. Strobl, E. Merkle, 2017, Psychometrika.

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

A. Zeileis, C. Strobl, J. Kopf, 2013, Psychometrika.

A New Stopping Criterion for Rasch Trees Based on the Mantel–Haenszel Effect Size Measure for Differential Item Functioning

C. Strobl, Rudolf Debelak, Mirka Henninger, 2021, Educational and psychological measurement.

Score‐based measurement invariance checks for Bayesian maximum‐a‐posteriori estimates in item response theory

C. Strobl, E. Merkle, Rudolf Debelak, 2020, The British journal of mathematical and statistical psychology.

Conditional permutation importance revisited

C. Strobl, Dries Debeer, 2020, BMC Bioinformatics.

Unbiased split selection for classification trees based on the Gini Index

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

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

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

An AUC-based permutation variable importance measure for random forests

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

Tree-Based Global Model Tests for Polytomous Rasch Models

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

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

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

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

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

Forest management and regional tree composition drive the host preference of saproxylic beetle communities

C. Strobl, M. Eugster, A. Floren, 2015 .

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

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

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

Carolin Strobl, C. Strobl, 2008 .

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..

Same data, different conclusions: Radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis

Laura Winther Balling, Sarah M. G. Otner, Patrick K. O'Neill, 2021, Organizational Behavior and Human Decision Processes.

Optimal classifier selection and negative bias in error rate estimation: an empirical study on high-dimensional prediction

Anne-Laure Boulesteix, Carolin Strobl, A. Boulesteix, 2009, BMC medical research methodology.

Score-Based Tests of Differential Item Functioning via Pairwise Maximum Likelihood Estimation

Achim Zeileis, Ting Wang, Carolin Strobl, 2017, Psychometrika.

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

Achim Zeileis, Carolin Strobl, Ting Wang, 2016 .

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

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

Anchor methods for DIF detection: A comparison of the iterative forward, backward, constant and all-other anchor class

Achim Zeileis, Carolin Strobl, Julia Kopf, 2013 .

A Framework for Anchor Methods and an Iterative Forward Approach for DIF Detection

A. Zeileis, C. Strobl, J. Kopf, 2015, Applied psychological measurement.

Anchor Selection Strategies for DIF Analysis

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

An AUC-based permutation variable importance measure for random forests

A. Boulesteix, C. Strobl, Silke Janitza, 2013, BMC Bioinformatics.

Variable Selection in Classification Trees Based on Imprecise Probabilities

C. Strobl, 2005, ISIPTA.

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

Carolin Strobl, C. Strobl, 2005 .

Party on! - A new, conditional variable importance measure for random forests available in party

Achim Zeileis, Carolin Strobl, A. Zeileis, 2009 .

What Affects the Quality of Score Transformations? Potential Issues in True-Score Equating Using the Partial Credit Model

C. Strobl, Rudolf Debelak, Carolina Fellinghauer, 2023, Educational and psychological measurement.

A new variable importance measure for random forests with missing data

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

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

Achim Zeileis, Carolin Strobl, Torsten Hothorn, 2009 .

Alternative methods to quantify variable importance in ecology

Falk Huettmann, Carolin Strobl, Steffen Oppel, 2009 .

Party on!

T. Hothorn, A. Zeileis, C. Strobl, 2009, R J..

(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..

PREDICTORS OF DEPRESSION AMONG MIDDLE-AGED AND OLDER MEN AND WOMEN IN EUROPE: A MACHINE LEARNING APPROACH

C. Strobl, L. Feliciano, S. Aichele, 2022, Innovation in aging.

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..

Flexible Rasch Mixture Models with Package psychomix

Achim Zeileis, Carolin Strobl, Friedrich Leisch, 2012 .

A Toolkit for Stability Assessment of Tree-Based Learners

Achim Zeileis, Michel Philipp, Carolin Strobl, 2016 .

Anchor Point Selection: Scale Alignment Based on an Inequality Criterion

A. Zeileis, C. Strobl, T. Oertzen, 2021, Applied psychological measurement.

Random Forests with Missing Values in the Covariates

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

[Automated breast ultrasound vs. handheld ultrasound: BI-RADS classification, duration of the examination and patient comfort].

C. Strobl, H. Prosch, G. Mostbeck, 2011, Ultraschall in der Medizin.

Optimal classifier selection and negative bias in error rate estimation: an empirical study on high-dimensional prediction

A. Boulesteix, C. Strobl, Carolin Strobl, 2009, BMC medical research methodology.

Party on! A new, conditional variable importance measure available in the party package

Achim Zeileis, Torsten Hothorn, Carolin Strobl, 2009 .

Rasch Mixture Models for DIF Detection

A. Zeileis, C. Strobl, Hannah Frick, 2015, Educational and psychological measurement.

Psychometric Mixture Models

F. Leisch, A. Zeileis, C. Strobl, 2015 .

Psychoco: Psychometric Computing in R

Achim Zeileis, Carolin Strobl, Florian Wickelmaier, 2012 .

Anchor point selection: An approach for anchoring without anchor items

Achim Zeileis, Carolin Strobl, Julia Kopf, 2018 .

A Framework for Anchor Methods and an Iterative Forward Approach for DIF Detection

A. Zeileis, C. Strobl, J. Kopf, 2015, Applied psychological measurement.

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

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

Interpretable machine learning for psychological research: Opportunities and pitfalls.

C. Strobl, Rudolf Debelak, Mirka Henninger, 2023, Psychological methods.

Measuring the Stability of Results From Supervised Statistical Learning

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

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

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

Measurement and Predictors of a Negative Attitude towards Statistics among LMU Students

F. Leisch, C. Strobl, C. Seiler, 2010 .

Julia Kopf , Thomas Augustin and Carolin Strobl The Potential of Model-Based Recursive Partitioning in the Social Sciences – Revisiting Ockham

C. Strobl, Thomas Augustin, J. Kopf, 2010 .

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

Carolin Strobl, Thomas Augustin, Julia Kopf, 2010 .

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

A. Boulesteix, T. Hothorn, A. Zeileis, 2007, BMC Bioinformatics.

Variable Selection Bias in Classification Trees Based on Imprecise Probabilities

Carolin Strobl, C. Strobl, 2005 .