generated by bibbase.org
  2020 (1)
Progressive or simple? A corpus-based study of aspect in World Englishes. Hundt, M.; Rautionaho, P.; and Strobl, C. Corpora. 2020.
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  2019 (3)
Fitting Prediction Rule Ensembles to Psychological Research Data: An Introduction and Tutorial. Fokkema, M.; and Strobl, C. Technical Report 2019.
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A comparison of aggregation rules for selecting anchor items in multi group DIF analysis. Huelmann, T.; Debelak, R.; and Strobl, C. Journal of Educational Measurement. 2019.
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Investigating Measurement Invariance by Means of Parameter Instability Tests for 2PL and 3PL Models. Debelak, R.; and Strobl, C. Educational and Psychological Measurement, 79(2): 385–398. 2019.
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  2018 (4)
Positionspapier zur Rolle der Psychologischen Methodenlehre in Forschung und Lehre. Meiser, T.; Eid, M.; Carstensen, C.; Erdfelder, E.; Gollwitzer, M.; Pohl, S.; Steyer, R.; and Strobl, C. Psychologische Rundschau, 69: 325–331. 2018.
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On the Estimation of Standard Errors in Cognitive Diagnosis Models. Philipp, M.; Strobl, C.; Torre, J. d. l.; and Zeileis, A. Journal of Educational and Behavioral Statistics, 43(1): 88–115. 2018.
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Measuring the Stability of Results from Supervised Statistical Learning. Philipp, M.; Rusch, T.; Hornik, K.; and Strobl, C. Journal of Computational and Graphical Statistics, 27(4): 685–700. 2018.
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Score-Based Tests of Differential Item Functioning via Pairwise Maximum Likelihood Estimation. Wang, T.; Strobl, C.; Zeileis, A.; and Merkle, E. C. Psychometrika,132–155. 2018.
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  2017 (2)
A First Survey on the Diversity of the R Community. Bollmann, S.; Cook, D.; Dumas, J.; Fox, J.; Josse, J.; Keyes, O.; Strobl, C.; Turner, H; and Debelak, R. The R Journal, 9(2): 541–552. 2017.
A First Survey on the Diversity of the R Community [pdf]Paper   link   bibtex  
Tree-Based Global Model Tests for Polytomous Rasch Models. Komboz, B.; Strobl, C.; and Zeileis, A. Educational and Psychological Measurement, 78(1): 128–166. 2017.
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  2016 (1)
A Toolkit for Stability Assessment of Tree-Based Learners. Philipp, M.; Zeileis, A.; and Strobl, C. In Colubi, I. A.; Blanco, A.; and Gatu, C., editor(s), Proceedings of COMPSTAT 2016 – 22nd International Conference on Computational Statistics, pages 315–325, Oviedo, 2016. The International Statistical Institute/International Association for Statistical Computing
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  2015 (5)
A Framework for Anchor Methods and an Iterative Forward Approach for DIF Detection. Kopf, J.; Zeileis, A.; and Strobl, C. Applied Psychological Measurement, 39(2): 83–103. 2015.
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Rasch Mixture Models for DIF Detection: A Comparison of Old and New Score Specifications. Frick, H.; Strobl, C.; and Zeileis, A. Educational and Psychological Measurement, 75(2): 208–234. 2015.
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Forest management and regional tree composition drive the host preference of saproxylic beetle communities. Mueller, J.; Wende, B.; Strobl, C.; Eugster, M.; Gallenberger, I.; Floren, A.; Steffan-Dewenter, I.; Linsenmair, K. E.; Weisser, W. W.; and Gossner, M. M. Journal of Applied Ecology, 52(3): 753–762. 2015.
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Anchor Selection Strategies for DIF Analysis: Review, Assessment, and New Approaches. Kopf, J.; Zeileis, A.; and Strobl, C. Educational and Psychological Measurement, 75(1): 22–56. 2015.
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Rasch trees: A New Method for Detecting Differential Item Functioning in the Rasch Model. Strobl, C.; Kopf, J.; and Zeileis, A. Psychometrika, 80(2): 289–316. 2015.
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  2014 (4)
Discussion to Wei-Yin Lohs "Fifty Years of Classification and Regression Trees". Strobl, C. International Statistical Review, 82(3): 349–352. 2014.
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(Psycho-)Analysis of Benchmark Experiments - A Formal Framework for Investigating the Relationship between Data Sets and Learning Algorithms. Eugster, M.; Leisch, F.; and Strobl, C. Computational Statistics & Data Analysis, 71(SI): 986–1000. 2014.
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Letter to the Editor: On the term 'interaction' and related phrases in the literature on Random Forests. Boulesteix, A.; Janitza, S.; Hapfelmeier, A.; Van Steen, K.; and Strobl, C. Briefings in Bioinformatics, 16(2): 338–345. 2014.
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A new variable importance measure for random forests with missing data. Hapfelmeier, A.; Hothorn, T.; Ulm, K.; and Strobl, C. Statistics and Computing, 24(1): 21–34. 2014.
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  2013 (4)
The Potential of Model-Based Recursive Partitioning in the Social Sciences: Revisiting Ockam's Razor. Kopf, J.; Augustin, T.; and Strobl, C. In McArdle, J.; and Ritschard, G., editor(s), Contemporary Issues in Exploratory Data Mining, pages 75–95. Routeledge, New York, 2013. Section: 3
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Data Mining. Strobl, C. In Little, T., editor(s), The Oxford Handbook on Quantitative Methods, pages 678–700. Oxford University Press USA, New York, 2013. Section: 29
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Rasch-Analyse des Freiburger Fragebogens zur Achtsamkeit. Sauer, S.; Strobl, C.; Walach, H.; and Kohls, N. Diagnostica, 59(2): 86–99. 2013.
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An AUC-based Permutation Variable Importance Measure for Random Forests. Janitza, S.; Strobl, C.; and Boulesteix, A. BMC Bioinformatics, 14(119). 2013.
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  2012 (4)
Das Rasch-Modell - Eine verständliche Einführung für Studium und Praxis. Strobl, C. Rainer Hampp Verlag, München, Mering, 2. erweiterte Auflage edition, 2012.
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Flexible Rasch Mixture Models with Package psychomix. Frick, H.; Strobl, C.; Leisch, F.; and Zeileis, A. Journal of Statistical Software, 48(7): 1–25. 2012.
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Random Forest Gini Importance Favours SNPs with Large Minor Allele Frequency: Impact, Sources and Recommendations. Boulesteix, A.; Bender, A.; Bermejo, J. L.; and Strobl, C. Briefings in Bioinformatics, 13(3): 292–304. 2012.
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Psychoco: Psychometric Computing in R. Wickelmaier, F.; Strobl, C.; and Zeileis, A. Journal of Statistical Software, 48(7): 1–5. 2012.
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  2011 (3)
Contributions to Psychometric Computing and Machine Learning. Strobl, C. Ph.D. Thesis, Department of Statistics, Ludwig-Maximilians-Universität München, Germany, 2011.
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Using the raschtree function for detecting differential item functioning in the Rasch model. Strobl, C.; Kopf, J.; and Zeileis, A. 2011.
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Accounting for Individual Differences in Bradley-Terry Models by Means of Recursive Partitioning. Strobl, C.; Wickelmaier, F.; and Zeileis, A. Journal of Educational and Behavioral Statistics, 36(2): 135–153. 2011.
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  2010 (5)
Random Forests with Missing Values in the Covariates. Rieger, A.; Hothorn, T.; and Strobl, C. Technical Report 79, Department of Statistics, Ludwig-Maximilians-Universität München, Germany, 2010.
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Wissen Frauen weniger oder nur das Falsche? – Ein statistisches Modell für unterschiedliche Aufgaben-Schwierigkeiten in Teilstichproben. Strobl, C.; Kopf, J.; and Zeileis, A. In Trepte, S.; and Verbeet, M., editor(s), Allgemeinbildung in Deutschland – Erkenntnisse aus dem SPIEGEL Studentenpisa-Test, pages 255–272. VS Verlag, Wiesbaden, 2010.
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Advances in Social Science Research Using R (Book Review). Strobl, C. Journal of Statistical Software, 34(2): 1–2. 2010.
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Das Rasch-Modell - Eine verständliche Einführung für Studium und Praxis. Strobl, C. Rainer Hampp Verlag, München, Mering, 2010.
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The Behaviour of Random Forest Permutation-Based Variable Importance Measures under Predictor Correlation. Nicodemus, K.; Malley, J.; Strobl, C.; and Ziegler, A. BMC Bioinformatics, 11(110): 1471–2105. 2010.
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  2009 (6)
Measurement and Predictors of a Negative Attitude towards Statistics among LMU Students. Strobl, C.; Dittrich, C.; Seiler, C.; Hackensperger, S.; and Leisch, F. In Kneib, T.; and Tutz, G., editor(s), Statistical Modelling and Regression Structures, pages 217–230. Springer, Berlin, 2009.
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Generalisierte lineare Modelle. Tutz, G.; and Strobl, C. In Holling, H.; and Schmitz, B., editor(s), Handbuch der Psychologie, Band 13: Handbuch Statistik, Methoden und Evaluation, pages 461–472. Hogrefe, Göttingen, 2009.
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Optimal Classifier Selection and Negative Bias in Error Rate Estimation: An Empirical Study on High-Dimensional Prediction. Boulesteix, A.; and Strobl, C. BMC Medical Research Methodology, 9(85): 1471–2288. 2009.
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Party on! A New, Conditional Variable Importance Measure for Random Forests Available in the party Package. Strobl, C.; Hothorn, T.; and Zeileis, A. The R Journal, 1(2): 14–17. 2009.
Party on! A New, Conditional Variable Importance Measure for Random Forests Available in the party Package [pdf]Paper   link   bibtex  
An Introduction to Recursive Partitioning: Rationale, Application and Characteristics of Classification and Regression Trees, Bagging and Random Forests. Strobl, C.; Malley, J.; and Tutz, G. Psychological Methods, 14(4): 323–348. 2009.
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Adaptive Selection of Extra Cutpoints – An Approach Towards Reconciling Robustness and Interpretability in Classification Trees. Strobl, C.; and Augustin, T. Journal of Statistical Theory and Practice, 3(1): 119–135. 2009.
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  2008 (5)
Statistical Issues in Machine Learning – Towards Reliable Split Selection and Variable Importance Measures. Strobl, C. Ph.D. Thesis, Department of Statistics, Ludwig-Maximilians-Universität München, Germany, 2008.
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Danger: High Power! – Exploring the Statistical Properties of a Test for Random Forest Variable Importance. Strobl, C.; and Zeileis, A. In Brito, P., editor(s), Proceedings of the 18th International Conference on Computational Statistics, Porto, Portugal (CD-ROM), pages 59–66, Heidelberg, 2008. Springer
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Evaluating Microarray-Based Classifiers: An Overview. Boulesteix, A.; Strobl, C.; Augustin, T.; and Daumer, M. Cancer Informatics, 6: 77–97. 2008.
Evaluating Microarray-Based Classifiers: An Overview [link]Paper   link   bibtex  
Conditional Variable Importance for Random Forests. Strobl, C.; Boulesteix, A.; Kneib, T.; Augustin, T.; and Zeileis, A. BMC Bioinformatics, 9(307): 1471–2105. 2008.
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Analysis of the Individual and Aggregate Genetic Contributions of Previously Identified SPINK5 , KLK7 and FLG Polymorphisms to Eczema Risk. 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.; and Smith, G. The Journal of Allergy and Clinical Immunology, 122(3): 560–568. 2008.
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  2007 (4)
Unbiased Split Selection for Classification Trees Based on the Gini Index. Strobl, C.; Boulesteix, A.; and Augustin, T. Computational Statistics & Data Analysis, 52(1): 483–501. 2007.
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Maximally Selected Chi-Square Statistics and Non-Monotonic Associations: An Exact Approach Based on Two Cutpoints. Boulesteix, A.; and Strobl, C. Computational Statistics & Data Analysis, 51(12): 6295–6306. 2007.
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Bias in random forest variable importance measures: Illustrations, sources and a solution. Strobl, C.; Boulesteix, A.; Zeileis, A.; and Hothorn, T. BMC Bioinformatics, 8(25): 1471–2105. 2007.
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Multiple Testing for SNP-SNP Interactions. Boulesteix, A.; Strobl, C.; Weidinger, S.; Wichmann, H. E.; and Wagenpfeil, S. Statistical Applications in Genetics and Molecular Biology, 6(1): 37. 2007.
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  2006 (1)
Interactive Statistics for the Behavioral Sciences (Book Review). Augustin, T.; and Strobl, C. Biometrics, 62: 625–626. 2006.
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  2005 (2)
Statistical Sources of Variable Selection Bias in Classification Trees Based on the Gini Index. Strobl, C. Technical Report 420, Department of Statistics, Ludwig-Maximilians-Universität München, Germany, 2005.
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Variable Selection in Classification Trees Based on Imprecise Probabilities. Strobl, C. In Cozman, F.; Nau, R.; and Seidenfeld, T., editor(s), Proceedings of the Fourth International Symposium on Imprecise Probabilities and Their Applications, Pittsburgh, 2005.
Variable Selection in Classification Trees Based on Imprecise Probabilities [link]Paper   link   bibtex  
  2004 (1)
Variable Selection Bias in Classification Trees. Strobl, C. Ph.D. Thesis, Department of Statistics, Ludwig-Maximilians-Universität München, Germany, 2004.
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  2002 (1)
Experimental Study on the Relationship between Perceived Binocular Direction and Distance. Strobl, C. Ph.D. Thesis, Department of Psychology, Universität Regensburg, Germany, 2002.
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  undefined (5)
stablelearner: A Toolkit for Stability Assessment of Tree-Based Learners. Philipp, M.; Strobl, C.; Zeilei, A.; Rusch, T.; and Hornik, K. .
stablelearner: A Toolkit for Stability Assessment of Tree-Based Learners [link]Paper   link   bibtex  
party: A Laboratory for Recursive Part(y)itioning. Hothorn, T.; Hornik, K.; Strobl, C.; and Zeileis, A. .
party: A Laboratory for Recursive Part(y)itioning [link]Paper   link   bibtex  
psychotree: Recursive Partitioning Based on Psychometric Models. Zeileis, A.; Strobl, C.; Wickelmaier, F.; and Kopf, J. .
psychotree: Recursive Partitioning Based on Psychometric Models [link]Paper   link   bibtex  
psychotools: Infrastructure for Psychometric Modeling. Zeileis, A.; Strobl, C.; Wickelmaier, F.; Komboz, B.; Kopf, J.; Schneider, L.; and Debelak, R. .
psychotools: Infrastructure for Psychometric Modeling [link]Paper   link   bibtex  
psychomix: Psychometric Mixture Models. Frick, H.; Strobl, C.; Leisch, F.; and Zeileis, A. .
psychomix: Psychometric Mixture Models [link]Paper   link   bibtex