Publications in peer reviewed journals

Strobl, C., Kopf, J., Kohler, L., von Oertzen, T. & Zeileis, A. (2020). Anchor point selection: An approach for anchoring without anchor items. Applied Psychological Measurement, .
[bib] [technical report]

Debeer, D. & Strobl, C. (2020). Conditional permutation importance revisited. BMC Bioinformatics, 21(1), 307.
[bib] [doi:10.1186/s12859-020-03622-2]

Hundt, M., Rautionaho, P. & Strobl, C. (2020). Progressive or simple? A corpus-based study of aspect in World Englishes. Corpora, 15(1), 77-106.
[bib] [technical report]

Fokkema, M. & Strobl, C. (2020). Fitting Prediction Rule Ensembles to Psychological Research Data: An Introduction and Tutorial. Psychological Methods, 25(5), 636-652.
[bib] [doi:doi:10.1037/met0000256] [technical report]

Huelmann, T., Debelak, R. & Strobl, C. (2020). A comparison of aggregation rules for selecting anchor items in multi group DIF analysis. Journal of Educational Measurement, 57(2), 185-215.
[bib] [doi:10.1111/jedm.12246]

Debelak, R. & Strobl, C. (2019). Investigating Measurement Invariance by Means of Parameter Instability Tests for 2PL and 3PL Models. Educational and Psychological Measurement, 79(2), 385-398. .
[bib] [doi:10.1177/0013164418777784]

Philipp, M., Rusch, T., Hornik, K. & Strobl, C. (2018). Measuring the Stability of Results from Supervised Statistical Learning. Journal of Computational and Graphical Statistics, 27(4), 685-700.
[bib] [doi:10.1080/10618600.2018.1473779] [technical report]

Philipp, M., Strobl, C., de la Torre, J. & Zeileis, A. (2018). On the Estimation of Standard Errors in Cognitive Diagnosis Models. Journal of Educational and Behavioral Statistics, 43(1), 88-115.
[bib] [doi:10.3102/1076998617719728] [technical report]

Wang, T., Strobl, C., Zeileis, A. & Merkle, E. C. (2018). Score-Based Tests of Differential Item Functioning via Pairwise Maximum Likelihood Estimation. Psychometrika, , 132-155.
[bib] [doi:10.1007/s11336-017-9591-8] [technical report]

Komboz, B., Strobl, C. & Zeileis, A. (2017). Tree-Based Global Model Tests for Polytomous Rasch Models. Educational and Psychological Measurement, 78(1), 128-166.
[bib] [doi:10.1177/0013164416664394] [technical report]

Frick, H., Strobl, C. & Zeileis, A. (2015). Rasch Mixture Models for DIF Detection: A Comparison of Old and New Score Specifications. Educational and Psychological Measurement, 75(2), 208-234.
[bib] [doi:10.1177/0013164414536183] [technical report]

Kopf, J., Zeileis, A. & Strobl, C. (2015). A Framework for Anchor Methods and an Iterative Forward Approach for DIF Detection. Applied Psychological Measurement, 39(2), 83-103.
[bib] [doi:10.1177/0146621614544195] [technical report]

Kopf, J., Zeileis, A. & Strobl, C. (2015). Anchor Selection Strategies for DIF Analysis: Review, Assessment, and New Approaches. Educational and Psychological Measurement, 75(1), 22-56.
[bib] [doi:10.1177/0013164414529792] [technical report]

Mueller, J., Wende, B., Strobl, C., Eugster, M., Gallenberger, I., Floren, A., Steffan-Dewenter, I., Linsenmair, K. E., Weisser, W. W. & Gossner, M. M. (2015). Forest management and regional tree composition drive the host preference of saproxylic beetle communities. Journal of Applied Ecology, 52(3), 753-762.
[bib] [doi:10.1111/1365-2664.12421]

Strobl, C., Kopf, J. & Zeileis, A. (2015). Rasch trees: A New Method for Detecting Differential Item Functioning in the Rasch Model. Psychometrika, 80(2), 289-316.
[bib] [doi:10.1007/s11336-013-9388-3] [technical report]

Boulesteix, A.-L., Janitza, S., Hapfelmeier, A., Van Steen, K. & Strobl, C. (2014). Letter to the Editor: On the term 'interaction' and related phrases in the literature on Random Forests. Briefings in Bioinformatics, 16(2), 338-345.
[bib] [doi:10.1093/bib/bbu012]

Eugster, M., Leisch, F. & Strobl, C. (2014). (Psycho-)Analysis of Benchmark Experiments - A Formal Framework for Investigating the Relationship between Data Sets and Learning Algorithms. Computational Statistics & Data Analysis, 71(SI), 986-1000.
[bib] [doi:10.1016/j.csda.2013.08.007] [technical report]

Hapfelmeier, A., Hothorn, T., Ulm, K. & Strobl, C. (2014). A new variable importance measure for random forests with missing data. Statistics and Computing, 24(1), 21-34.
[bib] [doi:10.1007/s11222-012-9349-1]

Janitza, S., Strobl, C. & Boulesteix, A.-L. (2013). An AUC-based Permutation Variable Importance Measure for Random Forests. BMC Bioinformatics, 14(119).
[bib] [doi:10.1186/1471-2105-14-119]

Sauer, S., Strobl, C., Walach, H. & Kohls, N. (2013). Rasch-Analyse des Freiburger Fragebogens zur Achtsamkeit. Diagnostica, 59(2), 86-99.
[bib] [doi:10.1026/0012-1924/a000084]

Boulesteix, A.-L., Bender, A., Lorenzo Bermejo, J. & Strobl, C. (2012). Random Forest Gini Importance Favours SNPs with Large Minor Allele Frequency: Impact, Sources and Recommendations. Briefings in Bioinformatics, 13(3), 292-304.
[bib] [doi:10.5282/ubm/epub.12224]

Frick, H., Strobl, C., Leisch, F. & Zeileis, A. (2012). Flexible Rasch Mixture Models with Package psychomix. Journal of Statistical Software, 48(7), 1-25.
[bib] [doi:10.18637/jss.v048.i07]

Wickelmaier, F., Strobl, C. & Zeileis, A. (2012). Psychoco: Psychometric Computing in R. Journal of Statistical Software, 48(7), 1-5.
[bib] [doi:10.18637/jss.v048.i01]

Strobl, C., Wickelmaier, F. & Zeileis, A. (2011). Accounting for Individual Differences in Bradley-Terry Models by Means of Recursive Partitioning. Journal of Educational and Behavioral Statistics, 36(2), 135-153.
[bib] [doi:10.5282/ubm/epub.10588]

Nicodemus, K., Malley, J., Strobl, C. & Ziegler, A. (2010). The Behaviour of Random Forest Permutation-Based Variable Importance Measures under Predictor Correlation. BMC Bioinformatics, 11(110), 1471-2105.
[bib] [doi:10.1186/1471-2105-11-110]

Boulesteix, A.-L. & 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), 1471-2288.
[bib] [doi:10.1186/1471-2288-9-85]

Strobl, C. & Augustin, T. (2009). Adaptive Selection of Extra Cutpoints -- An Approach Towards Reconciling Robustness and Interpretability in Classification Trees. Journal of Statistical Theory and Practice, 3(1), 119-135.
[bib] [doi:10.1080/15598608.2009.10411915]

Strobl, C., Hothorn, T. & Zeileis, A. (2009). Party on! A New, Conditional Variable Importance Measure for Random Forests Available in the party Package. The R Journal, 1(2), 14-17.
[bib] [url]

Strobl, C., Malley, J. & Tutz, G. (2009). An Introduction to Recursive Partitioning: Rationale, Application and Characteristics of Classification and Regression Trees, Bagging and Random Forests. Psychological Methods, 14(4), 323-348.
[bib] [doi:10.5282/ubm/epub.10589]

Boulesteix, A.-L., Strobl, C., Augustin, T. & Daumer, M. (2008). Evaluating Microarray-Based Classifiers: An Overview. Cancer Informatics, 6, 77-97.
[bib] [url]

Strobl, C., Boulesteix, A.-L., Kneib, T., Augustin, T. & Zeileis, A. (2008). Conditional Variable Importance for Random Forests. BMC Bioinformatics, 9(307), 1471-2105.
[bib] [doi:10.1186/1471-2105-9-307]

Strobl, C., Weidinger, S., Baurecht, H., Wagenpfeil, S., Henderson, J., Novak, N., Sandilands, A., Chen, H., Rodriguez, E., O'Regan, G., Watson, R., Liao, H., Zhao, Y., Barker, J., Allen, M., Reynolds, N., Meggit, S., Northstone, K. & Smith, G. (2008). Analysis of the Individual and Aggregate Genetic Contributions of Previously Identified SPINK5 , KLK7 and FLG Polymorphisms to Eczema Risk. The Journal of Allergy and Clinical Immunology, 122(3), 560-568.
[bib] [doi:10.1016/j.jaci.2008.05.050]

Boulesteix, A.-L. & Strobl, C. (2007). Maximally Selected Chi-Square Statistics and Non-Monotonic Associations: An Exact Approach Based on Two Cutpoints. Computational Statistics & Data Analysis, 51(12), 6295-6306.
[bib] [doi:10.1016/j.csda.2007.01.017]

Boulesteix, A.-L., Strobl, C., Weidinger, S., Wichmann, H. E. & Wagenpfeil, S. (2007). Multiple Testing for SNP-SNP Interactions. Statistical Applications in Genetics and Molecular Biology, 6(1), 37.
[bib] [doi:10.2202/1544-6115.1315]

Strobl, C., Boulesteix, A.-L. & Augustin, T. (2007). Unbiased Split Selection for Classification Trees Based on the Gini Index. Computational Statistics & Data Analysis, 52(1), 483-501.
[bib] [doi:10.1016/j.csda.2006.12.030] [technical report]

Strobl, C., Boulesteix, A.-L., Zeileis, A. & Hothorn, T. (2007). Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinformatics, 8(25), 1471-2105.
[bib] [doi:10.1186/1471-2105-8-25]

Publications in peer reviewed conference proceedings and edited volumes

Philipp, M., Zeileis, A. & Strobl, C. (2016). "A Toolkit for Stability Assessment of Tree-Based Learners." Proceedings of COMPSTAT 2016 – 22nd International Conference on Computational Statistics, edited by I. A. Colubi, A. Blanco & C. Gatu, Oviedo.
[bib] [technical report]

Frick, H., Strobl, C. & Zeileis, A. (2014). "To Split or to Mix? Tree vs. Mixture Models for Detecting Subgroups." COMPSTAT 2014 -- Proceedings in Computational Statistics, edited by M. Gilli, G. González-Rodríguez & A. Nieto-Reyes.
[bib] [technical report]

Strobl, C., Kopf, J. & Zeileis, A. (2010). Wissen Frauen weniger oder nur das Falsche? -- Ein statistisches Modell für unterschiedliche Aufgaben-Schwierigkeiten in Teilstichproben. In S. Trepte & M. Verbeet (Eds.). Allgemeinbildung in Deutschland -- Erkenntnisse aus dem SPIEGEL Studentenpisa-Test (255-272). Wiesbaden: VS Verlag.
[bib]

Strobl, C. & Zeileis, A. (2008). "Danger: High Power! -- Exploring the Statistical Properties of a Test for Random Forest Variable Importance." Proceedings of the 18th International Conference on Computational Statistics, Porto, Portugal (CD-ROM), edited by P. Brito, Heidelberg.
[bib] [technical report]

Strobl, C. (2005). "Variable Selection in Classification Trees Based on Imprecise Probabilities." Proceedings of the Fourth International Symposium on Imprecise Probabilities and Their Applications, edited by F. Cozman, R. Nau & T. Seidenfeld, Pittsburgh.
[bib] [url]

Invited contributions

Gloor, J., Strobl, C. & Debelak, R. (2020). DSI Insights: Wege aus der Angst vor Algorithmen. Inside IT Kolumne, .
[bib] [url]

Meiser, T., Eid, M., Carstensen, C., Erdfelder, E., Gollwitzer, M., Pohl, S., Steyer, R. & Strobl, C. (2018). Positionspapier zur Rolle der Psychologischen Methodenlehre in Forschung und Lehre. Psychologische Rundschau, 69, 325-331.
[bib] [doi:10.1026/0033-3042/a000417]

Bollmann, S., Cook, D., Dumas, J., Fox, J., Josse, J., Keyes, O., Strobl, C., Turner, H. & Debelak, R. (2017). A First Survey on the Diversity of the R Community. The R Journal, 9(2), 541-552.
[bib] [url]

Strobl, C. (2014). Discussion to Wei-Yin Lohs "Fifty Years of Classification and Regression Trees". International Statistical Review, 82(3), 349-352.
[bib]

Kopf, J., Augustin, T. & Strobl, C. (2013). The Potential of Model-Based Recursive Partitioning in the Social Sciences: Revisiting Ockam's Razor. In J. McArdle & G. Ritschard (Eds.). Contemporary Issues in Exploratory Data Mining (75-95). New York: Routeledge.
[bib]

Strobl, C. (2013). Data Mining. In T. Little (Ed.). The Oxford Handbook on Quantitative Methods (678-700). New York: Oxford University Press USA.
[bib]

Strobl, C. (2010). Advances in Social Science Research Using R (Book Review). Journal of Statistical Software, 34(2), 1-2.
[bib] [doi:10.18637/jss.v034.b02]

Strobl, C., Dittrich, C., Seiler, C., Hackensperger, S. & Leisch, F. (2009). Measurement and Predictors of a Negative Attitude towards Statistics among LMU Students. In T. Kneib & G. Tutz (Eds.). Statistical Modelling and Regression Structures (217-230). Berlin: Springer.
[bib]

Tutz, G. & Strobl, C. (2009). Generalisierte lineare Modelle. In H. Holling & B. Schmitz (Eds.). Handbuch der Psychologie, Band 13: Handbuch Statistik, Methoden und Evaluation (461-472). Göttingen: Hogrefe.
[bib]

Augustin, T. & Strobl, C. (2006). Interactive Statistics for the Behavioral Sciences (Book Review). Biometrics, 62, 625-626.
[bib] [doi:10.1111/j.1541-0420.2006.00589_1.x]

Books

Strobl, C. (2012). Das Rasch-Modell - Eine verständliche Einführung für Studium und Praxis. München, Mering: Rainer Hampp Verlag.
[bib]

Strobl, C. (2010). Das Rasch-Modell - Eine verständliche Einführung für Studium und Praxis. München, Mering: Rainer Hampp Verlag.
[bib]

Software

Debeer, D., Hothorn, T. & Strobl, C. permimp: Conditional Permutation Importance.
[bib] [url]

Philipp, M., Strobl, C., Zeileis, A., Rusch, T. & Hornik, K. stablelearner: A Toolkit for Stability Assessment of Tree-Based Learners.
[bib] [url]

Frick, H., Strobl, C., Leisch, F. & Zeileis, A. psychomix: Psychometric Mixture Models.
[bib] [url]

Zeileis, A., Strobl, C., Wickelmaier, F., Komboz, B., Kopf, J., Schneider, L. & Debelak, R. psychotools: Infrastructure for Psychometric Modeling.
[bib] [url]

Zeileis, A., Strobl, C., Wickelmaier, F. & Kopf, J. psychotree: Recursive Partitioning Based on Psychometric Models.
[bib] [url]

Hothorn, T., Hornik, K., Strobl, C. & Zeileis, A. party: A Laboratory for Recursive Part(y)itioning.
[bib] [url]

Theses

Strobl, C. (2002). Experimental Study on the Relationship between Perceived Binocular Direction and Distance. Department of Psychology, Universität Regensburg, Germany.
[bib]

Strobl, C. (2004). Variable Selection Bias in Classification Trees. Department of Statistics, Ludwig-Maximilians-Universität München, Germany.
[bib]

Strobl, C. (2008). Statistical Issues in Machine Learning -- Towards Reliable Split Selection and Variable Importance Measures. Department of Statistics, Ludwig-Maximilians-Universität München, Germany.
[bib]

Strobl, C. (2011). Contributions to Psychometric Computing and Machine Learning. Department of Statistics, Ludwig-Maximilians-Universität München, Germany.
[bib]

Miscellaneous

Schneider, L., Zeileis, A. & Strobl, C. (2020). Descriptive and Graphical Analysis of the Variable and Cutpoint Selection inside Random Forests.
[bib] [url]

Strobl, C., Kopf, J. & Zeileis, A. (2011). Using the raschtree function for detecting differential item functioning in the Rasch model.
[bib] [url]

Rieger, A., Hothorn, T. & Strobl, C. (2010). Random Forests with Missing Values in the Covariates. Department of Statistics, Ludwig-Maximilians-Universität München, Germany.
[bib]

Strobl, C. (2005). Statistical Sources of Variable Selection Bias in Classification Trees Based on the Gini Index. Department of Statistics, Ludwig-Maximilians-Universität München, Germany.
[bib]

Strobl, C. & Reimer, R. Studierendenvertretung der Ludwig-Maximilians-Universität München, G. (Ed.). (2004). Akzeptanz von Studiengebühren für das Erststudium .
[bib]