Author Bühlmann, Peter
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2010 | Journal Article |
Model-based Boosting 2.0
Hothorn, T.; Bühlmann, P.; Kneib, T. ; Schmid, M. & Hofner, B. (2010)
Journal of Machine Learning Reseach - Machine Learning Open Source Software, 11 pp. 2109-2113.
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2013 | Journal Article
Conditional transformation models
Hothorn, T.; Kneib, T. & Bühlmann, P. (2013)
Journal of the Royal Statistical Society: Series B (Statistical Methodology), 76(1) pp. 3-27. DOI: https://doi.org/10.1111/rssb.12017
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2014 | Journal Article
Discussion of "The Evolution of Boosting Algorithms" and "Extending Statistical Boosting"
Bühlmann, P.; Gertheiss, J. ; Hieke, S.; Kneib, T. ; Ma, S.; Schumacher, M. & Tutz, G. et al. (2014)
Methods of Information in Medicine, 53(6) pp. 436-445. DOI: https://doi.org/10.3414/13100122
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2020 | Journal Article | Research Paper
Seeded intervals and noise level estimation in change point detection: a discussion of Fryzlewicz (2020)
Kovács, S.; Li, H. & Bühlmann, P. (2020)
Journal of the Korean Statistical Society, 49(4) pp. 1081-1089. DOI: https://doi.org/10.1007/s42952-020-00077-2
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2020 | Preprint
Seeded Binary Segmentation: A general methodology for fast and optimal change point detection
Kovács, S.; Li, H. ; Bühlmann, P.& Munk, A. (2020)
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2020 | Preprint
Optimistic search strategy: Change point detection for large-scale data via adaptive logarithmic queries
Kovács, S.; Li, H. ; Haubner, L.; Munk, A. & Bühlmann, P. (2020)
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2022 | Journal Article |
Distributional regression modeling via generalized additive models for location, scale, and shape: An overview through a data set from learning analytics
Marmolejo‐Ramos, F.; Tejo, M.; Brabec, M.; Kuzilek, J.; Joksimovic, S.; Kovanovic, V. & González, J. et al. (2022)
Wiley Interdisciplinary Reviews. Data Mining and Knowledge Discovery,. DOI: https://doi.org/10.1002/widm.1479
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