mboost

R package mboost: Model-Based Boosting. Functional gradient descent algorithm (boosting) for optimizing general risk functions utilizing component-wise (penalised) least squares estimates or regression trees as base-learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data.


References in zbMATH (referenced in 61 articles , 2 standard articles )

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  1. Kolosova, Tanya; Berestizhevsky, Samuel: Supervised machine learning. Optimization framework and applications with SAS and R (2021)
  2. Engebretsen, Solveig; Glad, Ingrid K.: Additive monotone regression in high and lower dimensions (2019)
  3. Vicendese, D.; Te Marvelde, L.; McNair, P. D.; Whitfield, K.; English, D. R.; Ben Taieb, S.; Hyndman, R. J.; Thomas, R.: Predicting the whole distribution with methods for depth data analysis demonstrated on a colorectal cancer treatment study (2019)
  4. Welchowski, Thomas; Schmid, Matthias: Sparse kernel deep stacking networks (2019)
  5. Bender, Andreas; Groll, Andreas; Scheipl, Fabian: A generalized additive model approach to time-to-event analysis (2018)
  6. Emily Morris, Kevin He, Yanming Li, Yi Li, Jian Kang: SurvBoost: An R Package for High-Dimensional Variable Selection in the Stratified Proportional Hazards Model via Gradient Boosting (2018) arXiv
  7. Mayr, Andreas; Hofner, Benjamin: Boosting for statistical modelling-a non-technical introduction (2018)
  8. Schauberger, Gunther; Groll, Andreas: Predicting matches in international football tournaments with random forests (2018)
  9. Seibold, Heidi; Bernau, Christoph; Boulesteix, Anne-Laure; De Bin, Riccardo: On the choice and influence of the number of boosting steps for high-dimensional linear Cox-models (2018)
  10. Thomas, Janek; Mayr, Andreas; Bischl, Bernd; Schmid, Matthias; Smith, Adam; Hofner, Benjamin: Gradient boosting for distributional regression: faster tuning and improved variable selection via noncyclical updates (2018)
  11. Waldmann, Elisabeth: Quantile regression: a short story on how and why (2018)
  12. Bommert, Andrea; Rahnenführer, Jörg; Lang, Michel: A multicriteria approach to find predictive and sparse models with stable feature selection for high-dimensional data (2017)
  13. Brockhaus, Sarah; Melcher, Michael; Leisch, Friedrich; Greven, Sonja: Boosting flexible functional regression models with a high number of functional historical effects (2017)
  14. Friedrichs, Stefanie; Manitz, Juliane; Burger, Patricia; Amos, Christopher I.; Risch, Angela; Chang-Claude, Jenny; Wichmann, Heinz-Erich; Kneib, Thomas; Bickeböller, Heike; Hofner, Benjamin: Pathway-based kernel boosting for the analysis of genome-wide association studies (2017)
  15. Greven, Sonja; Scheipl, Fabian: A general framework for functional regression modelling (2017)
  16. Kraus, Daniel; Czado, Claudia: D-vine copula based quantile regression (2017)
  17. Mayr, Andreas; Hofner, Benjamin; Waldmann, Elisabeth; Hepp, Tobias; Meyer, Sebastian; Gefeller, Olaf: An update on statistical boosting in biomedicine (2017)
  18. Pande, Amol; Li, Liang; Rajeswaran, Jeevanantham; Ehrlinger, John; Kogalur, Udaya B.; Blackstone, Eugene H.; Ishwaran, Hemant: Boosted multivariate trees for longitudinal data (2017)
  19. Pierre-André Cornillon and Nicolas Hengartner and Eric Matzner-Løber: Iterative Bias Reduction Multivariate Smoothing in R: The ibr Package (2017) not zbMATH
  20. Reto Bürgin; Gilbert Ritschard: Coefficient-Wise Tree-Based Varying Coefficient Regression with vcrpart (2017) not zbMATH

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