Cited by 4,021Statistical Methods and InferenceData Mining Algorithms and ApplicationsStatistical Methods and Applications

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Unbiased Recursive Partitioning: A Conditional Inference Framework

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Hothorn, Torsten, Hornik, Kurt, Zeileis, Achim·2006·Journal of Computational and Graphical Statistics

DOI 10.1198/106186006x133933

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Abstract

Recursive binary partitioning is a popular tool for regression analysis. Two fundamental problems of exhaustive search procedures usually applied to fit such models have been known for a long time: overfitting and a selection bias towards covariates with many possible splits or missing values. While pruning procedures are able to solve the overfitting problem, the variable selection bias still seriously affects the interpretability of tree-structured regression models. For some special cases unbiased procedures have been suggested, however lacking a common theoretical foundation. We propose a unified framework for recursive partitioning which embeds tree-structured regression models into a well defined theory of conditional inference procedures. Stopping criteria based on multiple test procedures are implemented and it is shown that the predictive performance of the resulting trees is as good as the performance of established exhaustive search procedures. It turns out that the partitions and therefore the models induced by both approaches are structurally different, confirming the need for an unbiased variable selection. Moreover, it is shown that the prediction accuracy of trees with early stopping is equivalent to the prediction accuracy of pruned trees with unbiased variable selection. The methodology presented here is applicable to all kinds of regression problems, including nominal, ordinal, numeric, censored as well as multivariate response variables and arbitrary measurement scales of the covariates. Data from studies on glaucoma classification, node positive breast cancer survival and mammography experience are re-analyzed.

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Applied logistic regression
(1990) · Choice Reviews Online · doi:10.5860/choice.27-3338 · Cited by 35,305
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867
Untitled work
(1992) · Cited by 23,431
Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,030
UCI Repository of machine learning databases
Blake, C. L. (1998) · Medical Entomology and Zoology · Cited by 10,582
Categorical Data Analysis
Hapeman, D. E., & Agresti, A. (1991) · Technometrics · doi:10.1080/00401706.1991.10484817 · Cited by 9,407
Mathematical Methods of Statistics.
Geary, R. C., & Cramer, H. L. (1947) · The Economic Journal · doi:10.2307/2226151 · Cited by 6,095
An exploratory technique for investigating large quantities of categorical data
Kass, G. V. (1980) · Applied Statistics · doi:10.2307/2986296 · Cited by 2,880
UCI Repository of Machine Learning Databases
Merz, C. J. (1996) · Medical Entomology and Zoology · Cited by 2,363
MULTIVARIATE REGRESSION TREES: A NEW TECHNIQUE FOR MODELING SPECIES–ENVIRONMENT RELATIONSHIPS
De’ath, G. (2002) · Ecology · doi:10.1890/0012-9658(2002)083[1105:mrtant]2.0.co;2 · Cited by 1,275

Show all 57 references

Research works printed but not linked: 9

De'ath G., 2002, Ecology, 83, 1105
Dobra A., 2001, Proceedings of the Eighteenth International Conference on Machine Learning, San Francisco: Morgan Kaufmann, pp., 90
Frank E., 1998, Proceedings of the Fifteenth International Conference on Machine Learning, San Francisco: Morgan Kaufmann, pp., 152
Loh W.-Y., 2002, Statistica Sinica, 12, 361
Loh W.-Y., 1997, Statistica Sinica, 7, 815
Mingers J., 1987, Journal of the Operations Research Society, 38, 39
Peters A., 2002, R News, 2, 33
Schumacher M., 2001, J. Crowley, New York, Basel: Marcel Dekker, pp., 321
White A. P., 1994, Machine Learning, 15, 321

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Self-citation
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Authors: Hothorn, T., Hornik, K., & Zeileis, A.
Affiliations: Vienna University of Economics and Business (AT)
ORCID: Hothorn, Torsten
Hornik, Kurt
Zeileis, Achim
Venue: Journal of Computational and Graphical Statistics
Publisher: Informa UK Limited
Published: 2006-09
Volume: 15
Issue: 3
Pages: 651-674
ISSN: 1061-8600 (ISSN-L)
1537-2715
DOI: 10.1198/106186006x133933
OpenAlex: W2123998733
Cited by: 4,021
References: 57
Citations by year: 2026: 171 · 2025: 247 · 2024: 272 · 2023: 310 · 2022: 348 · 2021: 381 · 2020: 332 · 2019: 340 · 2018: 292 · 2017: 258 · 2016: 198 · 2015: 228 · 2014: 171 · 2013: 139 · 2012: 117
FWCI: 27.53
Type: journal article
Language: en
Open access: green
doi.org
Locations: Journal of Computational and Graphical Statistics (publishedVersion)
ePubWU Institutional Repository (Vienna University of Economics and Business) (submittedVersion)
WU Research (submittedVersion)
Zenodo (CERN European Organization for Nuclear Research)
Funders: Deutsche Forschungsgemeinschaft
Linköpings Universitet
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
Keywords: conditional inference, variable selection bias, overfitting, stopping rules, multiple comparisons, early stopping, tree pruning, missing data, prediction accuracy, censored responses, multivariate response

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