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 60 articles , 2 standard articles )

Showing results 1 to 20 of 60.
Sorted by year (citations)

1 2 3 next

  1. Engebretsen, Solveig; Glad, Ingrid K.: Additive monotone regression in high and lower dimensions (2019)
  2. 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)
  3. Welchowski, Thomas; Schmid, Matthias: Sparse kernel deep stacking networks (2019)
  4. Bender, Andreas; Groll, Andreas; Scheipl, Fabian: A generalized additive model approach to time-to-event analysis (2018)
  5. 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
  6. Mayr, Andreas; Hofner, Benjamin: Boosting for statistical modelling-a non-technical introduction (2018)
  7. Schauberger, Gunther; Groll, Andreas: Predicting matches in international football tournaments with random forests (2018)
  8. 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)
  9. 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)
  10. Waldmann, Elisabeth: Quantile regression: a short story on how and why (2018)
  11. Bommert, Andrea; Rahnenführer, Jörg; Lang, Michel: A multicriteria approach to find predictive and sparse models with stable feature selection for high-dimensional data (2017)
  12. Brockhaus, Sarah; Melcher, Michael; Leisch, Friedrich; Greven, Sonja: Boosting flexible functional regression models with a high number of functional historical effects (2017)
  13. 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)
  14. Greven, Sonja; Scheipl, Fabian: A general framework for functional regression modelling (2017)
  15. Kraus, Daniel; Czado, Claudia: D-vine copula based quantile regression (2017)
  16. Mayr, Andreas; Hofner, Benjamin; Waldmann, Elisabeth; Hepp, Tobias; Meyer, Sebastian; Gefeller, Olaf: An update on statistical boosting in biomedicine (2017)
  17. Pande, Amol; Li, Liang; Rajeswaran, Jeevanantham; Ehrlinger, John; Kogalur, Udaya B.; Blackstone, Eugene H.; Ishwaran, Hemant: Boosted multivariate trees for longitudinal data (2017)
  18. Pierre-André Cornillon and Nicolas Hengartner and Eric Matzner-Løber: Iterative Bias Reduction Multivariate Smoothing in R: The ibr Package (2017) not zbMATH
  19. Reto Bürgin; Gilbert Ritschard: Coefficient-Wise Tree-Based Varying Coefficient Regression with vcrpart (2017) not zbMATH
  20. Ternès, Nils; Rotolo, Federico; Heinze, Georg; Michiels, Stefan: Identification of biomarker-by-treatment interactions in randomized clinical trials with survival outcomes and high-dimensional spaces (2017)

1 2 3 next