Publikationen in begutachteten Zeitschriften

Schweinsberg, M., Feldman, M., Staub, N., van den Akker, O. R., van Aert, R., Van Assen, M. A., Liu, Y., Althoff, T., Heer, J., Kale, A., ..., Strobl, C. & others (2021). Same Data, Different Conclusions: Radical Dispersion in Empirical Results When Independent Analysts Operationalize and Test the Same Hypothesis. Organizational Behavior and Human Decision Processes, .
[bib] [doi:https://doi.org/10.1016/j.obhdp.2021.02.003] [url]

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]

Publikationen in begutachteten Konferenz- und Sammelbänden

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]

Eingeladene Beiträge

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]

Bücher

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]

Qualifikationsarbeiten

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]

Weitere Publikationen

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]