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2020 – today
- 2023
[j31]David Rügamer, Philipp F. M. Baumann
, Thomas Kneib, Torsten Hothorn:
Probabilistic time series forecasts with autoregressive transformation models. Stat. Comput. 33(2): 37 (2023)- 2022
- [j30]Lucas Kook, Lisa Herzog, Torsten Hothorn, Oliver Dürr, Beate Sick:
Deep and interpretable regression models for ordinal outcomes. Pattern Recognit. 122: 108263 (2022) - [i6]Lucas Kook, Andrea Götschi, Philipp F. M. Baumann, Torsten Hothorn, Beate Sick:
Deep interpretable ensembles. CoRR abs/2205.12729 (2022) - [i5]Gabriele Campanella, Lucas Kook, Ida Häggström, Torsten Hothorn, Thomas J. Fuchs:
Deep conditional transformation models for survival analysis. CoRR abs/2210.11366 (2022) - 2021
- [j29]Torsten Hothorn, Achim Zeileis:
Predictive Distribution Modeling Using Transformation Forests. J. Comput. Graph. Stat. 30(4): 1181-1196 (2021) - [j28]Lucas Kook, Torsten Hothorn:
Regularized Transformation Models: The tramnet Package. R J. 13(1): 581 (2021) - [j27]Bálint Tamási, Torsten Hothorn:
tramME: Mixed-Effects Transformation Models Using Template Model Builder. R J. 13(2): 306 (2021) - [c7]Philipp F. M. Baumann, Torsten Hothorn, David Rügamer:
Deep Conditional Transformation Models. ECML/PKDD (3) 2021: 3-18 - [i4]David Rügamer, Philipp F. M. Baumann, Thomas Kneib, Torsten Hothorn:
Transforming Autoregression: Interpretable and Expressive Time Series Forecast. CoRR abs/2110.08248 (2021) - 2020
- [j26]Torsten Hothorn:
Transformation boosting machines. Stat. Comput. 30(1): 141-152 (2020) - [c6]Beate Sick, Torsten Hothorn, Oliver Dürr:
Deep transformation models: Tackling complex regression problems with neural network based transformation models. ICPR 2020: 2476-2481 - [i3]Beate Sick, Torsten Hothorn, Oliver Dürr:
Deep transformation models: Tackling complex regression problems with neural network based transformation models. CoRR abs/2004.00464 (2020) - [i2]Philipp F. M. Baumann, Torsten Hothorn, David Rügamer:
Deep Conditional Transformation Models. CoRR abs/2010.07860 (2020) - [i1]Lucas Kook, Lisa Herzog, Torsten Hothorn, Oliver Dürr, Beate Sick:
Ordinal Neural Network Transformation Models: Deep and interpretable regression models for ordinal outcomes. CoRR abs/2010.08376 (2020)
2010 – 2019
- 2019
- [j25]Heidi Seibold, Torsten Hothorn, Achim Zeileis:
Generalised linear model trees with global additive effects. Adv. Data Anal. Classif. 13(3): 703-725 (2019) - 2016
- [j24]Benjamin Hofner, Thomas Kneib, Torsten Hothorn:
A unified framework of constrained regression. Stat. Comput. 26(1-2): 1-14 (2016) - 2015
- [j23]Torsten Hothorn, Achim Zeileis:
Partykit: a modular toolkit for recursive partytioning in R. J. Mach. Learn. Res. 16: 3905-3909 (2015) - 2014
- [j22]Alexander Hapfelmeier, Torsten Hothorn, Kurt Ulm, Carolin Strobl:
A new variable importance measure for random forests with missing data. Stat. Comput. 24(1): 21-34 (2014) - 2013
- [j21]Benjamin Hofner, Torsten Hothorn, Thomas Kneib:
Variable selection and model choice in structured survival models. Comput. Stat. 28(3): 1079-1101 (2013) - 2012
- [j20]Alexander Hapfelmeier, Torsten Hothorn, Kurt Ulm:
Recursive partitioning on incomplete data using surrogate decisions and multiple imputation. Comput. Stat. Data Anal. 56(6): 1552-1565 (2012) - 2011
- [j19]Torsten Hothorn, Friedrich Leisch:
Case studies in reproducibility. Briefings Bioinform. 12(3): 288-300 (2011) - [c5]Friedrich Leisch, Manuel J. A. Eugster, Torsten Hothorn:
Executable Papers for the R Community: The R2 Platform for Reproducible Research. ICCS 2011: 618-626 - 2010
- [j18]Anne-Laure Boulesteix, Torsten Hothorn:
Testing the additional predictive value of high-dimensional molecular data. BMC Bioinform. 11: 78 (2010) - [j17]Torsten Hothorn, Peter Bühlmann, Thomas Kneib, Matthias Schmid, Benjamin Hofner:
Model-based Boosting 2.0. J. Mach. Learn. Res. 11: 2109-2113 (2010) - [j16]Peter Bühlmann, Torsten Hothorn:
Twin Boosting: improved feature selection and prediction. Stat. Comput. 20(2): 119-138 (2010) - [j15]Matthias Schmid, Sergej Potapov, Annette B. Pfahlberg, Torsten Hothorn:
Estimation and regularization techniques for regression models with multidimensional prediction functions. Stat. Comput. 20(2): 139-150 (2010)
2000 – 2009
- 2009
- [j14]Xuefei Mi, Tetsuhisa Miwa, Torsten Hothorn:
New Numerical Algorithm for Multivariate Normal Probabilities in Package mvtnorm. R J. 1(1): 37 (2009) - [j13]Carolin Strobl, Torsten Hothorn, Achim Zeileis:
Party on! R J. 1(2): 14 (2009) - 2008
- [j12]Matthias Schmid, Torsten Hothorn:
Flexible boosting of accelerated failure time models. BMC Bioinform. 9 (2008) - [j11]Matthias Schmid, Torsten Hothorn:
Boosting additive models using component-wise P-Splines. Comput. Stat. Data Anal. 53(2): 298-311 (2008) - 2007
- [j10]Carolin Strobl, Anne-Laure Boulesteix, Achim Zeileis, Torsten Hothorn:
Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinform. 8 (2007) - [j9]Gabriele Wolz, Anton Nömayr, Torsten Hothorn, Joachim Hornegger, Wolfgang Römer, Werner Bautz, Torsten Kuwert:
Comparison of performance between rigid and non-rigid software registering CT to FDG-PET. Int. J. Comput. Assist. Radiol. Surg. 2(3-4): 183-190 (2007) - 2006
- [j8]Torsten Hothorn, Peter Bühlmann:
Model-based boosting in high dimensions. Bioinform. 22(22): 2828-2829 (2006) - 2005
- [j7]A. Peters, Torsten Hothorn, Berthold Lausen:
Generalised indirect classifiers. Comput. Stat. Data Anal. 49(3): 849-861 (2005) - [j6]Torsten Hothorn, Berthold Lausen:
Bundling classifiers by bagging trees. Comput. Stat. Data Anal. 49(4): 1068-1078 (2005) - [j5]Radim Chrástek, Matthias Wolf, Klaus Donath, Heinrich Niemann, Dietrich Paulus, Torsten Hothorn, Berthold Lausen, Robert Lämmer, Christian Y. Mardin, Georg Michelson:
Automated segmentation of the optic nerve head for diagnosis of glaucoma. Medical Image Anal. 9(4): 297-314 (2005) - 2004
- [j4]David Meyer, Friedrich Leisch, Torsten Hothorn, Kurt Hornik:
StatDataML: An XML Format for Statistical Data. Comput. Stat. 19(3): 493-509 (2004) - 2003
- [b1]Torsten Hothorn:
Bundling classifiers with an application to glaucoma diagnosis. Technical University of Dortmund, Germany, 2003, pp. 1-107 - [j3]Torsten Hothorn, Berthold Lausen:
Bagging tree classifiers for laser scanning images: a data- and simulation-based strategy. Artif. Intell. Medicine 27(1): 65-79 (2003) - [j2]Torsten Hothorn, Berthold Lausen:
On the exact distribution of maximally selected rank statistics. Comput. Stat. Data Anal. 43(2): 121-137 (2003) - [j1]Torsten Hothorn, Berthold Lausen:
Double-bagging: combining classifiers by bootstrap aggregation. Pattern Recognit. 36(6): 1303-1309 (2003) - [c4]Radim Chrástek, Matthias Wolf, Klaus Donath, Heinrich Niemann, Torsten Hothorn, Berthold Lausen, Robert Lämmer, Christian Y. Mardin, Georg Michelson:
Automated Segmentation of the Optic Nerve Head for Glaucoma Diagnosis. Bildverarbeitung für die Medizin 2003: 338-342 - 2002
- [c3]Torsten Hothorn, Berthold Lausen:
Bagging Tree Classifiers for Glaucoma Diagnosis. COMPSTAT 2002: 183-188 - [c2]Torsten Hothorn, Kurt Hornik:
Exact Nonparametric Inference in R. COMPSTAT 2002: 355-360 - [c1]David Meyer, Friedrich Leisch, Torsten Hothorn, Kurt Hornik:
StatDataML: An XML Format for Statistical Data. COMPSTAT 2002: 545-550
Coauthor Index
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