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 25 articles , 2 standard articles )

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  1. Pande, Amol; Li, Liang; Rajeswaran, Jeevanantham; Ehrlinger, John; Kogalur, Udaya B.; Blackstone, Eugene H.; Ishwaran, Hemant: Boosted multivariate trees for longitudinal data (2017)
  2. De Bin, Riccardo: Boosting in Cox regression: a comparison between the likelihood-based and the model-based approaches with focus on the R-packages \itCoxBoost and \itmboost (2016)
  3. Dubossarsky, E.; Friedman, J.H.; Ormerod, J.T.; Wand, M.P.: Wavelet-based gradient boosting (2016)
  4. Hofner, Benjamin; Kneib, Thomas; Hothorn, Torsten: A unified framework of constrained regression (2016)
  5. Reulen, Holger; Kneib, Thomas: Boosting multi-state models (2016)
  6. Sweeney, Elizabeth; Crainiceanu, Ciprian; Gertheiss, Jan: Testing differentially expressed genes in dose-response studies and with ordinal phenotypes (2016)
  7. Tutz, Gerhard; Koch, Dominik: Improved nearest neighbor classifiers by weighting and selection of predictors (2016)
  8. Tutz, Gerhard; Schmid, Matthias: Modeling discrete time-to-event data (2016)
  9. Leha, Andreas: Statistical methods to enhance clinical prediction with high-dimensional data and ordinal response (2015)
  10. Binder, Harald (ed.); Kestler, Hans A. (ed.); Schmid, Matthias (ed.): Proceedings of Reisensburg 2011 (2014)
  11. Hofner, Benjamin; Mayr, Andreas; Robinzonov, Nikolay; Schmid, Matthias: Model-based boosting in R: a hands-on tutorial using the R package mboost (2014)
  12. Hofner, Benjamin; Hothorn, Torsten; Kneib, Thomas: Variable selection and model choice in structured survival models (2013)
  13. Zhao, Yanchang: R and data mining. Examples and case studies (2013)
  14. Hofner, Benjamin: Boosting in structured additive models. (2012)
  15. Scheipl, Fabian; Fahrmeir, Ludwig; Kneib, Thomas: Spike-and-slab priors for function selection in structured additive regression models (2012)
  16. Tutz, Gerhard; Petry, Sebastian: Nonparametric estimation of the link function including variable selection (2012)
  17. Fenske, Nora; Kneib, Thomas; Hothorn, Torsten: Identifying risk factors for severe childhood malnutrition by boosting additive quantile regression (2011)
  18. Johnson, Brent A.; Long, Qi: Survival ensembles by the sum of pairwise differences with application to lung cancer microarray studies (2011)
  19. Williams, Graham: Data Mining with Rattle and R. The art of excavating data for knowledge discovery. (2011)
  20. Hothorn, Torsten; Bühlmann, Peter; Kneib, Thomas; Schmid, Matthias; Hofner, Benjamin: Model-based boosting 2.0 (2010)

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