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

The DOI matches its Crossref record; 62 of the 62 references it cites were found in a registry or an index.

Unbiased Recursive Partitioning: A Conditional Inference Framework

Open access

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

DOI 10.1198/106186006x133933

Open this paper in CoChat — free account

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.

Integrity signals

Registry

Crossref · 10.1198/106186006x133933

Cites

62 of 62 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

The text we found for this paper looks like another document, so its claims and self-check weren’t run. The registry and citation checks still apply.

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The text we found for this paper looks like another document, so the self-check wasn’t run.

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

Sorted by citations

Items 501–600 of 1,011

Binary recursive partitioning: Background, methods, and application to psychology
Merkle, E. C., & Shaffer, V. A. (2010) · British Journal of Mathematical and Statistical Psychology · doi:10.1348/000711010x503129 · Cited by 63
Hypothesis‐driven species distribution models for tree species in the Bavarian Alps
Mellert, K. H., Fensterer, V., Küchenhoff, H., Reger, B., Kölling, C., Klemmt, H.-J., & Ewald, J. (2011) · Journal of Vegetation Science · doi:10.1111/j.1654-1103.2011.01274.x · Cited by 64
A Pathway Based Classification Method for Analyzing Gene Expression for Alzheimer’s Disease Diagnosis
Voyle, N., Keohane, A., Newhouse, S., Lunnon, K., Johnston, C., Soininen, H. S., Kłoszewska, I., Mecocci, P., Tsolaki, M., Vellas, B. J., et al. (2015) · Journal of Alzheimer s Disease · doi:10.3233/jad-150440 · Cited by 63
Can we predict biological condition of stream ecosystems? A multi-stressors approach linking three biological indices to physico-chemistry, hydromorphology and land use
Villeneuve, B., Souchon, Y., Usseglio‐Polatera, P., Ferréol, M., & Valette, L. (2014) · Ecological Indicators · doi:10.1016/j.ecolind.2014.07.016 · Cited by 64
Habitat loss, fragmentation and degradation effects on small mammals: Analysis with conditional inference tree statistical modelling
Johnstone, C. P., Lill, A., & Reina, R. D. (2014) · Biological Conservation · doi:10.1016/j.biocon.2014.04.025 · Cited by 64
REGRESSION TREES FOR LONGITUDINAL AND MULTIRESPONSE DATAPreprint
Loh, W., & Zheng, W. (2016) · Cited by 64
High resolution synthetic residential energy use profiles for the United States
Thorve, S., Baek, Y. Y., Swarup, S., Mortveit, H., Marathe, A., Vullikanti, A. K. S., & MARATHE, M. V. (2023) · Scientific Data · doi:10.1038/s41597-022-01914-1 · Cited by 64
Predictive Analytics of Insurance Claims Using Multivariate Decision Trees
Quan, Z., & Valdez, E. A. (2018) · Dependence Modeling · doi:10.1515/demo-2018-0022 · Cited by 63
Who Are Conspiracy Theorists? A Comprehensive Approach to Explaining Conspiracy Beliefs
Enders, A. M., & Smallpage, S. M. (2019) · Social Science Quarterly · doi:10.1111/ssqu.12711 · Cited by 63
The effectiveness of employee retention through an uplift modeling approach
Rombaut, E., & Guerry, M. (2020) · International Journal of Manpower · doi:10.1108/ijm-04-2019-0184 · Cited by 96
Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms
Mantovani, R. G., Horváth, T., Rossi, A. L. D., Cerri, R., Barbon, S., Vanschoren, J., & de Carvalho, A. C. P. L. F. (2024) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-024-01002-5 · Cited by 121
Gene Selection Using Iterative Feature Elimination Random Forests for Survival Outcomes
Pang, H., George, S. L., Hui, K. Y., & Tong, T. (2012) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2012.63 · Cited by 65
Emergency doctors' strategies to manage competing workload demands in an interruptive environment: An observational workflow time study
Walter, S. R., Raban, M. Z., Dunsmuir, W. T. M., Douglas, H., & Westbrook, J. I. (2016) · Applied Ergonomics · doi:10.1016/j.apergo.2016.07.020 · Cited by 68
Comparison of 14 different families of classification algorithms on 115 binary datasetsPreprint
Wainer, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.00930 · Cited by 63
Random Forest Modelling of High-Dimensional Mixed-Type Data for Breast Cancer Classification
Quist, J., Taylor, L., Staaf, J., & Grigoriadis, A. E. (2021) · Cancers · doi:10.3390/cancers13050991 · Cited by 67
Identifying global and local drivers of change in mangrove cover and the implications for management
Maina, J. M., Bosire, J. O., Kairo, J. G., Bandeira, S., Mangora, M. M., Macamo, C. D. C. F., Ralison, H. O., & Gamoyo, M. J. (2021) · Global Ecology and Biogeography · doi:10.1111/geb.13368 · Cited by 62
Who Supports Political Violence?
Armaly, M., & Enders, A. M. (2022) · Perspectives on Politics · doi:10.1017/s1537592722001086 · Cited by 62
Anthropogenic refugia ameliorate the severe climate-related decline of a montane mammal along its trailing edge
Morelli, T. L., Smith, A. B., Kastely, C. R., Mastroserio, I., Moritz, C. C., & Beissinger, S. R. (2012) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2012.1301 · Cited by 61
Relating Fire-Caused Change in Forest Structure to Remotely Sensed Estimates of Fire Severity
Lydersen, J. M., Collins, B. M., Miller, J. D., Fry, D. L., & Stephens, S. L. (2016) · Fire Ecology · doi:10.4996/fireecology.1203099 · Cited by 62
Games and big data: A scalable multi-dimensional churn prediction model
Bertens, P., Guitart, A., & Perianez, A. (2017) · IEEE Conference on Computational Intelligence and Games (CIG) · doi:10.1109/cig.2017.8080412 · Cited by 64
Divergent habitat use of two urban lizard species
Winchell, K. M., Carlen, E. J., Puente‐Rolón, A. R., & Revell, L. J. (2017) · Ecology and Evolution · doi:10.1002/ece3.3600 · Cited by 62
Predicting Metabolic Syndrome With Machine Learning Models Using a Decision Tree Algorithm: Retrospective Cohort Study
Yu, C.-S., Lin, Y.-J., Lin, C.-H., Wang, S., Lin, S.-Y., Lin, S. H., Wu, J. L., & Chang, S.-S. (2020) · JMIR Medical Informatics · doi:10.2196/17110 · Cited by 66
partykit: A Toolkit for Recursive Partytioning
Hothorn, T., & Zeileis, A. (2011) · doi:10.32614/cran.package.partykit · Cited by 62
Self-citation
Quantitative clinical characteristics of 53 patients with MPS VII: a cross-sectional analysis
Zielonka, M., Garbade, S. F., Kölker, S., Hoffmann, G. F., & Ries, M. (2017) · Genetics in Medicine · doi:10.1038/gim.2017.10 · Cited by 61
Local and landscape drivers of biodiversity of four groups of ants in coffee landscapes
la Mora, A. D., Murnen, C. J., & Philpott, S. M. (2013) · Biodiversity and Conservation · doi:10.1007/s10531-013-0454-z · Cited by 61
Multi-temporal analysis reveals that predictors of mountain pine beetle infestation change during outbreak cycles
Walter, J. A., & Platt, R. V. (2013) · Forest Ecology and Management · doi:10.1016/j.foreco.2013.03.038 · Cited by 60
OncodriveROLE classifies cancer driver genes in loss of function and activating mode of action
Schroeder, M. P., Rubio-Pérez, C., Tamborero, D., González-Pérez, A., & López-Bigas, N. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu467 · Cited by 58
A Nearest Neighbour extension to project duration forecasting with Artificial Intelligence
Wauters, M., & Vanhoucke, M. (2016) · European Journal of Operational Research · doi:10.1016/j.ejor.2016.11.018 · Cited by 68
Geography of current and future global mammal extinction risk
Davidson, A. D., Shoemaker, K. T., Weinstein, B. G., Costa, G. C., Brooks, T. M., Ceballos, G., Radeloff, V. C., Rondinini, C., & Graham, C. H. (2017) · PLoS ONE · doi:10.1371/journal.pone.0186934 · Cited by 68
Heavy Metal(loids) in typical Chinese tobacco-growing soils: Concentrations, influence factors and potential health risks
Wu, H., Liu, Q., Ma, J., Liu, L., Qu, Y., Gong, Y., Yang, S., & Luo, T. (2019) · Chemosphere · doi:10.1016/j.chemosphere.2019.125591 · Cited by 61
Variability in vegetation and surface fuels across mixed-conifer-dominated landscapes with over 40 years of natural fire
Collins, B. M., Lydersen, J. M., Fry, D. L., Wilkin, K., Moody, T., & Stephens, S. L. (2016) · Forest Ecology and Management · doi:10.1016/j.foreco.2016.09.010 · Cited by 60
Critical importance of large native trees for conservation of a rare Neotropical epiphyte
Kartzinel, T. R., Trapnell, D. W., & Shefferson, R. P. (2013) · Journal of Ecology · doi:10.1111/1365-2745.12145 · Cited by 60
A comparative study of variable selection methods in the context of developing psychiatric screening instruments
Lu, F., & Petkova, E. (2013) · Statistics in Medicine · doi:10.1002/sim.5937 · Cited by 63
MELK expression in ovarian cancer correlates with poor outcome and its inhibition by OTSSP167 abrogates proliferation and viability of ovarian cancer cells
Kohler, R. S., Kettelhack, H., Knipprath-Mészáros, A. M., Fedier, A., Schoetzau, A., Jacob, F., & Heinzelmann-Schwarz, V. A. (2017) · Gynecologic Oncology · doi:10.1016/j.ygyno.2017.02.016 · Cited by 60
Predictive Factors of Surgical Outcome in Frontal Lobe Epilepsy Explored with Stereoelectroencephalography
Bonini, F., McGonigal, A., Scavarda, D., Carron, R., Régis, J. M., Dufour, H., Peragut, J. C., Laguitton, V., Villeneuve, N., Chauvel, P., et al. (2017) · Neurosurgery · doi:10.1093/neuros/nyx342 · Cited by 60
Long term effects of manual lymphatic drainage and active exercises on physical morbidities, lymphoscintigraphy parameters and lymphedema formation in patients operated due to breast cancer: A clinical trial
de Oliveira, M. M. F., Gurgel, M. S. C., Amorim, B. J., Ramos, C. D., Derchain, S., Furlan-Santos, N., dos Santos, C. C., & Sarian, L. O. (2018) · PLoS ONE · doi:10.1371/journal.pone.0189176 · Cited by 60
Integrated assessment of short-term direct and indirect economic flood impacts including uncertainty quantification
Sieg, T., Schinko, T., Vogel, K., Mechler, R., Merz, B., & Kreibich, H. (2019) · PLoS ONE · doi:10.1371/journal.pone.0212932 · Cited by 60
Antimony, beryllium, cobalt, and vanadium in urban park soils in Beijing: Machine learning-based source identification and health risk-based soil environmental criteria
Wu, Y., Liu, Q., Ma, J., Zhao, W., Chen, H., & Qu, Y. (2021) · Environmental Pollution · doi:10.1016/j.envpol.2021.118554 · Cited by 60
Living standards and mortality since the middle ages
Kelly, M., & Ó Gráda, C. (2014) · The Economic History Review · doi:10.1111/1468-0289.12023 · Cited by 59
The positioning of concessive adverbial clauses in English: assessing the importance of discourse-pragmatic and processing-based constraints
Wiechmann, D., & Kerz, E. (2013) · English Language and Linguistics · doi:10.1017/s1360674312000305 · Cited by 59
Facial Signals and Social Actions in Multimodal Face-to-Face Interaction
Nota, N., Trujillo, J. P., & Holler, J. (2021) · Brain Sciences · doi:10.3390/brainsci11081017 · Cited by 63
Incident Duration Prediction with Hybrid Tree-based Quantile Regression
He, Q., Kamarianakis, Y., Jintanakul, K., & Wynter, L. (2013) · Complex networks and dynamic systems · doi:10.1007/978-1-4614-6243-9_12 · Cited by 59
Relating fuel loads to overstorey structure and composition in a fire-excluded Sierra Nevada mixed conifer forest
Lydersen, J. M., Collins, B. M., Knapp, E. E., Roller, G. B., & Stephens, S. L. (2015) · International Journal of Wildland Fire · doi:10.1071/wf13066 · Cited by 59
Shifts in the suitable habitat available for brown trout (Salmo trutta L.) under short-term climate change scenarios
Muñoz‐Mas, R., Lopez‐Nicolas, A., Martínez‐Capel, F., & Pulido-Velázquez, M. (2015) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2015.11.147 · Cited by 59
Mortality Risk for Acute Cholangitis (MAC): a risk prediction model for in-hospital mortality in patients with acute cholangitis
Schneider, J., Hapfelmeier, A., Thöres, S., Obermeier, A., Schulz, C. U., Pförringer, D., Nennstiel, S., Spinner, C. D., Schmid, R. M., Algül, H., et al. (2016) · BMC Gastroenterology · doi:10.1186/s12876-016-0428-1 · Cited by 57
Assessing the performance of 38 machine learning models: the case of land consumption rates in Bavaria, Germany
Hagenauer, J., Omrani, H., & Helbich, M. (2019) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2019.1579333 · Cited by 64
A forgotten disease: Pelvic congestion syndrome as a cause of chronic lower abdominal pain
Jurga-Karwacka, A., Karwacki, G. M., Schoetzau, A., Zech, C. J., Heinzelmann-Schwarz, V. A., & Schwab, F. D. (2019) · PLoS ONE · doi:10.1371/journal.pone.0213834 · Cited by 59
Local geographic range predicts freshwater fish extinctions in Singapore
Giam, X., Ng, T. H., Lok, A. F. S. L., & Ng, H. H. (2011) · Journal of Applied Ecology · doi:10.1111/j.1365-2664.2010.01953.x · Cited by 59
Automatically detectable indicators of programming assignment difficulty
Ihantola, P., Sorva, J., & Vihavainen, A. (2014) · Conference on Information Technology Education · doi:10.1145/2656450.2656476 · Cited by 58
A Predictive Model for Postembolization Syndrome after Transarterial Hepatic Chemoembolization of Hepatocellular Carcinoma
Khalaf, M. H., H, V. S. M. P., Abdelrazek, M. A., Shah, R. P., Khosla, A., Jackson, K., Desai, M., & Kothary, N. (2018) · Radiology · doi:10.1148/radiol.2018180257 · Cited by 58
Sleep determines quality of life in autistic adults: A longitudinal study
Deserno, M., Borsboom, D., Begeer, S., van Rentergem, J. A. A., Mataw, K. J. S., & Geurts, H. (2019) · Autism Research · doi:10.1002/aur.2103 · Cited by 53
Morphosyntactic Variation
Callahan, E. (2019) · Cambridge University Press eBooks · doi:10.1017/9781316162316.010 · Cited by 58
Modeling of spatial stratified heterogeneity
Guo, J., Wang, J., Xu, C., & Song, Y. (2022) · GIScience & Remote Sensing · doi:10.1080/15481603.2022.2126375 · Cited by 58
Regularization approaches in clinical biostatistics: A review of methods and their applications
Friedrich, S., Groll, A., Ickstadt, K., Kneib, T., Pauly, M., Rahnenführer, J., & Friede, T. (2022) · Statistical Methods in Medical Research · doi:10.1177/09622802221133557 · Cited by 58
Environmental variability drives shifts in the foraging behaviour and reproductive success of an inshore seabird
Kowalczyk, N. D., Reina, R. D., Preston, T. J., & Chiaradia, A. (2015) · Oecologia · doi:10.1007/s00442-015-3294-6 · Cited by 62
Incidence of marine debris in seabirds feeding at different water depths
Tavares, D. C., de Moura, J. F., Merico, A., & Siciliano, S. J. (2017) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2017.04.012 · Cited by 65
Upward and downward bias when measuring inequality of opportunity
Brunori, P., Peragine, V., & Serlenga, L. (2018) · Social Choice and Welfare · doi:10.1007/s00355-018-1165-x · Cited by 72
A Brazilian cohort of individuals with Phelan-McDermid syndrome: genotype-phenotype correlation and identification of an atypical case
Costa, C. I. S., Branco, E. V., Monfardini, F., Ferraz, H. C., Fock, R. A., Barbosa, R. H. A., Pessoa, A. L. S., Pérez, A. B. A., Lourenço, N. C. V., Vibranovski, M. D., et al. (2019) · Journal of Neurodevelopmental Disorders · doi:10.1186/s11689-019-9273-1 · Cited by 59
Psychometric and Machine Learning Approaches to Reduce the Length of Scales
González, O. (2020) · Multivariate Behavioral Research · doi:10.1080/00273171.2020.1781585 · Cited by 63
Forest fire effects on stream water quality at continental scales: a meta-analysis
Hampton, T. B., Lin, S. G. M., & Basu, N. B. (2022) · Environmental Research Letters · doi:10.1088/1748-9326/ac6a6c · Cited by 64
Using conditional tree forests and life history traits to assess specific risks of stream degradation under multiple pressure scenario
Mondy, C. P., & Usseglio‐Polatera, P. (2013) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2013.05.072 · Cited by 56
Quantitative Measurements of Relative Fluid-Attenuated Inversion Recovery (FLAIR) Signal Intensities in Acute Stroke for the Prediction of Time from Symptom Onset
Cheng, B., Brinkmann, M., Forkert, N. D., Treszl, A., Ebinger, M., Köhrmann, M., Wu, O., Kang, D., Liebeskind, D. S., Tourdias, T., et al. (2012) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1038/jcbfm.2012.129 · Cited by 57
Impact of invasive plants in Mediterranean habitats: disentangling the effects of characteristics of invaders and recipient communities
Fried, G., Laitung, B., Pierre, C., Chagué, N., & Panetta, F. D. (2013) · Biological Invasions · doi:10.1007/s10530-013-0597-6 · Cited by 57
Analysis and correction of bias in Total Decrease in Node Impurity measures for tree-based algorithms
Sandri, M. F., & Zuccolotto, P. (2009) · Statistics and Computing · doi:10.1007/s11222-009-9132-0 · Cited by 57
Choline Kinase Alpha as an Androgen Receptor Chaperone and Prostate Cancer Therapeutic Target
Asim, M., Massie, C. E., Orafidiya, F. A., Pértega‐Gomes, N., Warren, A. Y., Esmaeili, M., Selth, L. A., Zecchini, H. I., Luko, K., Qureshi, A., et al. (2015) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djv371 · Cited by 58
Random forest and leaf multispectral reflectance data to differentiate three soybean varieties from two pigweeds
Fletcher, R. S., & Reddy, K. N. (2016) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2016.09.004 · Cited by 57
Censoring Unbiased Regression Trees and Ensembles
Steingrimsson, J., Diao, L., & Strawderman, R. (2018) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1407775 · Cited by 54
The Performance of Early-Generation Perennial Winter Cereals at 21 Sites across Four Continents
Hayes, R. C., Wang, S., Newell, M. T., Turner, M. K., Larsen, J., Gazza, L., Anderson, J. A., Bell, L. W., Cattani, D. J., Frels, K. A., et al. (2018) · Sustainability · doi:10.3390/su10041124 · Cited by 57
Application of kernel principal component analysis and computational machine learning to exploration of metabolites strongly associated with diet
Shiokawa, Y., Date, Y., & Kikuchi, J. (2018) · Scientific Reports · doi:10.1038/s41598-018-20121-w · Cited by 57
Analyzing Spatial Heterogeneity of Housing Prices Using Large Datasets
Wu, Y., Wei, Y. D., & Li, H. (2019) · Applied Spatial Analysis and Policy · doi:10.1007/s12061-019-09301-x · Cited by 59
Risk Factors Associated with Structural Progression in Normal-Tension Glaucoma: Intraocular Pressure, Systemic Blood Pressure, and Myopia
Lee, K., Yang, H., Kim, J. Y., Seong, G. J., Kim, C. Y., & Bae, H. W. (2020) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.61.8.35 · Cited by 57
Measuring the stability of results from supervised statistical learning
Philipp, M., Rusch, T., Hornik, K., & Strobl, C. (2018) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2018.1473779 · Cited by 56
Self-citation
Tree-Structured Mixed-Effects Regression Modeling for Longitudinal Data
Eo, S., & Cho, H. (2013) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2013.794732 · Cited by 56
Tundra Underlain By Thawing Permafrost Persistently Emits Carbon to the Atmosphere Over 15 Years of Measurements
Schuur, E. A. G., Bracho, R., Celis, G., Belshe, E. F., Ebert, C., Ledman, J., Mauritz, M., Pegoraro, E., Plaza, C., Rodenhizer, H., et al. (2021) · Journal of Geophysical Research Biogeosciences · doi:10.1029/2020jg006044 · Cited by 65
Consequences of technology and social innovation on traditional business model
Vrontis, D., Morea, D., Basile, G., Bonacci, I., & Mazzitelli, A. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.120877 · Cited by 58
Protective and risk factors in amateur equestrians and description of injury patterns: A retrospective data analysis and a case - control survey
Hasler, R. M., Gyssler, L., Benneker, L. M., Martinolli, L., Schötzau, A., Zimmermann, H., & Exadaktylos, A. K. (2011) · Journal of Trauma Management & Outcomes · doi:10.1186/1752-2897-5-4 · Cited by 56
Gender Differences in Machine Learning Models of Trauma and Suicidal Ideation in Veterans of the Iraq and Afghanistan Wars
Gradus, J. L., King, M. W., Galatzer‐Levy, I. R., & Street, A. E. (2017) · Journal of Traumatic Stress · doi:10.1002/jts.22210 · Cited by 57
Seasonal Variation in Vitamin D in Association with Age, Inflammatory Cytokines, Anthropometric Parameters, and Lifestyle Factors in Older Adults
Elizondo‐Montemayor, L., Castillo, E. C., López, C. E. R., Villarreal-Calderón, J. R., Gómez-Carmona, M., Tenorio-Martínez, S., Nieblas, B., & García‐Rivas, G. (2017) · Mediators of Inflammation · doi:10.1155/2017/5719461 · Cited by 56
Assessing the uncertainty of maize yield without nitrogen fertilization
Correndo, A. A., Rotundo, J. L., Tremblay, N., Archontoulis, S. V., Coulter, J. A., Diaz, D. A. R., Franzen, D., Franzluebbers, A. J., Nafziger, E. D., Schwalbert, R. A., et al. (2020) · Field Crops Research · doi:10.1016/j.fcr.2020.107985 · Cited by 56
Letter to the Editor: On the term 'interaction' and related phrases in the literature on Random Forests
Boulesteix, A., Janitza, S., Hapfelmeier, A., Steen, K. V., & Strobl, C. (2014) · Briefings in Bioinformatics · doi:10.1093/bib/bbu012 · Cited by 54
Severity classes in adults with hypermobile Ehlers–Danlos syndrome/hypermobility spectrum disorders: a pilot study of 105 Italian patients
Copetti, M., Morlino, S., Colombi, M., Grammatico, P., Fontana, A., & Castori, M. (2019) · Rheumatology · doi:10.1093/rheumatology/kez029 · Cited by 55
The ETS family member GABPα modulates androgen receptor signalling and mediates an aggressive phenotype in prostate cancer
Sharma, N. L., Massie, C. E., Butter, F., Mann, M., Bon, H., Ramos‐Montoya, A., Menon, S., Stark, R., Lamb, A. D. G., Scott, H. E., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku281 · Cited by 55
Diversity and feeding strategies of soil microfauna along elevation gradients in Himalayan cold deserts
Devetter, M., Háněl, L., Řeháková, K., & Doležal, J. (2017) · PLoS ONE · doi:10.1371/journal.pone.0187646 · Cited by 55
Early Prediction of University Dropouts – A Random Forest Approach
Behr, A., Giese, M., K., H. D. T., & Theune, K. (2020) · Jahrbücher für Nationalökonomie und Statistik · doi:10.1515/jbnst-2019-0006 · Cited by 55
Comparing hundreds of machine learning and discrete choice models for travel demand modeling: An empirical benchmark
Wang, S., Mo, B., Zheng, Y., Hess, S., & Zhao, J. (2024) · Transportation Research Part B Methodological · doi:10.1016/j.trb.2024.103061 · Cited by 88
Topographic and Bioclimatic Determinants of the Occurrence of Forest and Grassland in Tropical Montane Forest-Grassland Mosaics of the Western Ghats, India
Das, A., Nagendra, H., Anand, M., & Bunyan, M. (2015) · PLoS ONE · doi:10.1371/journal.pone.0130566 · Cited by 53
Conspiring to Mean: Experimental and Computational Evidence for a Usage-Based Harmonic Approach to Morphophonology
Kapatsinski, V. M. (2013) · Language · doi:10.1353/lan.2013.0003 · Cited by 55
Automated Detection of Forest Gaps in Spruce Dominated Stands Using Canopy Height Models Derived from Stereo Aerial Imagery
Zielewska-Büttner, K., Adler, P., Ehmann, M., & Braunisch, V. (2016) · Remote Sensing · doi:10.3390/rs8030175 · Cited by 55
Assessing the performance of urban forest carbon sequestration models using direct measurements of tree growth
Boukili, V., Bebber, D. P., Mortimer, T. A. L., Venicx, G., Lefcourt, D., Chandler, M., & Eisenberg, C. (2017) · Urban forestry & urban greening · doi:10.1016/j.ufug.2017.03.015 · Cited by 55
Personalized Risk Prediction in Clinical Oncology Research: Applications and Practical Issues Using Survival Trees and Random Forests
Hu, C., & Steingrimsson, J. A. (2017) · Journal of Biopharmaceutical Statistics · doi:10.1080/10543406.2017.1377730 · Cited by 55
Habitat foraging niche of a High Arctic zooplanktivorous seabird in a changing environment
Jakubas, D., Wojczulanis‐Jakubas, K., Iliszko, L. M., Strøm, H., & Stempniewicz, L. (2017) · Scientific Reports · doi:10.1038/s41598-017-16589-7 · Cited by 55
Tree‐based flood damage modeling of companies: Damage processes and model performance
Sieg, T., Vogel, K., Merz, B., & Kreibich, H. (2017) · Water Resources Research · doi:10.1002/2017wr020784 · Cited by 55
Stress, predators, and survival: Exploring permit (Trachinotus falcatus) catch-and-release fishing mortality in the Florida Keys
Holder, P. E., Griffin, L. P., Adams, A. J., Danylchuk, A. J., Cooke, S. J., & Brownscombe, J. W. (2019) · Journal of Experimental Marine Biology and Ecology · doi:10.1016/j.jembe.2019.151289 · Cited by 55
Predictive Blood-Based Biomarkers in Patients with Epithelial Ovarian Cancer Treated with Carboplatin and Paclitaxel with or without Bevacizumab: Results from GOG-0218
Secord, A. A., Burdett, K. B., Owzar, K., Tritchler, D., Sibley, A. B., Liu, Y., Starr, M. D., Brady, J. C., Lankes, H. A., Hurwitz, H. I., et al. (2020) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-19-0226 · Cited by 55
Decoding emotions in expressive music performances: A multi-lab replication and extension study
Akkermans, J., Schapiro, R., Müllensiefen, D., Jakubowski, K., Shanahan, D. T., Baker, D. J., Busch, V., Lothwesen, K., Elvers, P., Fischinger, T., et al. (2018) · Cognition & Emotion · doi:10.1080/02699931.2018.1541312 · Cited by 56
Early Prediction of Dropout and Final Exam Performance in an Online Statistics Course
Figueroa-Cañas, J., & Vinuesa, T. S. (2020) · IEEE Revista Iberoamericana de Tecnologias del Aprendizaje · doi:10.1109/rita.2020.2987727 · Cited by 61
Assessing and understanding non-responsiveness of maize and soybean to fertilizer applications in African smallholder farms
Roobroeck, D., Palm, C. A., Nziguheba, G., Weil, R. R., & Vanlauwe, B. (2020) · Agriculture Ecosystems & Environment · doi:10.1016/j.agee.2020.107165 · Cited by 54
One of the least disturbed marine coastal ecosystems on Earth: Spatial and temporal persistence of Darwin’s sub‐Antarctic giant kelp forests
Mora‐Soto, A., Capsey, A., Friedlander, A. M., Palacios, M., Brewin, P. E., Golding, N., Dayton, P. K., Tussenbroek, B. I. V., Montiel, A., Goodell, W., et al. (2021) · Journal of Biogeography · doi:10.1111/jbi.14221 · Cited by 56
Interpretable machine learning for psychological research: Opportunities and pitfalls.
Henninger, M., Debelak, R., Rothacher, Y., & Strobl, C. (2023) · Psychological Methods · doi:10.1037/met0000560 · Cited by 69
Classification Trees for Ordinal Responses inR: TherpartScorePackage
Galimberti, G., Soffritti, G., & Maso, M. D. (2012) · Journal of Statistical Software · doi:10.18637/jss.v047.i10 · Cited by 54

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Unbiased Recursive Partitioning: A Conditional Inference Framework

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks