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 67 articles , 2 standard articles )

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  1. Rui J. Costa, Moritz Gerstung: The R package ebmstate for disease progression analysis under empirical Bayes Cox models (2022) arXiv
  2. Ellenbach, Nicole; Boulesteix, Anne-Laure; Bischl, Bernd; Unger, Kristian; Hornung, Roman: Improved outcome prediction across data sources through robust parameter tuning (2021)
  3. Fasiolo, M., Wood, S. N., Zaffran, M., Nedellec, R., Goude, Y. : qgam: Bayesian Nonparametric Quantile Regression Modeling in R (2021) not zbMATH
  4. Jakob A. Dambon, Fabio Sigrist, Reinhard Furrer: varycoef: An R Package for Gaussian Process-based Spatially Varying Coefficient Models (2021) arXiv
  5. Kolosova, Tanya; Berestizhevsky, Samuel: Supervised machine learning. Optimization framework and applications with SAS and R (2021)
  6. Muggeo, Vito M. R.; Torretta, Federico; Eilers, Paul H. C.; Sciandra, Mariangela; Attanasio, Massimo: Multiple smoothing parameters selection in additive regression quantiles (2021)
  7. Yousuf, Kashif; Ng, Serena: Boosting high dimensional predictive regressions with time varying parameters (2021)
  8. Engebretsen, Solveig; Glad, Ingrid K.: Additive monotone regression in high and lower dimensions (2019)
  9. 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)
  10. Welchowski, Thomas; Schmid, Matthias: Sparse kernel deep stacking networks (2019)
  11. Bender, Andreas; Groll, Andreas; Scheipl, Fabian: A generalized additive model approach to time-to-event analysis (2018)
  12. 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
  13. Mayr, Andreas; Hofner, Benjamin: Boosting for statistical modelling-a non-technical introduction (2018)
  14. Schauberger, Gunther; Groll, Andreas: Predicting matches in international football tournaments with random forests (2018)
  15. 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)
  16. 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)
  17. Waldmann, Elisabeth: Quantile regression: a short story on how and why (2018)
  18. Bommert, Andrea; Rahnenführer, Jörg; Lang, Michel: A multicriteria approach to find predictive and sparse models with stable feature selection for high-dimensional data (2017)
  19. Brockhaus, Sarah; Melcher, Michael; Leisch, Friedrich; Greven, Sonja: Boosting flexible functional regression models with a high number of functional historical effects (2017)
  20. 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)

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