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 51 articles , 2 standard articles )

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  1. Engebretsen, Solveig; Glad, Ingrid K.: Additive monotone regression in high and lower dimensions (2019)
  2. Welchowski, Thomas; Schmid, Matthias: Sparse kernel deep stacking networks (2019)
  3. 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
  4. 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)
  5. 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)
  6. Bommert, Andrea; Rahnenführer, Jörg; Lang, Michel: A multicriteria approach to find predictive and sparse models with stable feature selection for high-dimensional data (2017)
  7. Brockhaus, Sarah; Melcher, Michael; Leisch, Friedrich; Greven, Sonja: Boosting flexible functional regression models with a high number of functional historical effects (2017)
  8. 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)
  9. Kraus, Daniel; Czado, Claudia: D-vine copula based quantile regression (2017)
  10. Mayr, Andreas; Hofner, Benjamin; Waldmann, Elisabeth; Hepp, Tobias; Meyer, Sebastian; Gefeller, Olaf: An update on statistical boosting in biomedicine (2017)
  11. Pande, Amol; Li, Liang; Rajeswaran, Jeevanantham; Ehrlinger, John; Kogalur, Udaya B.; Blackstone, Eugene H.; Ishwaran, Hemant: Boosted multivariate trees for longitudinal data (2017)
  12. Pierre-André Cornillon and Nicolas Hengartner and Eric Matzner-Løber: Iterative Bias Reduction Multivariate Smoothing in R: The ibr Package (2017) not zbMATH
  13. Reto Bürgin; Gilbert Ritschard: Coefficient-Wise Tree-Based Varying Coefficient Regression with vcrpart (2017) not zbMATH
  14. 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)
  15. De Bin, Riccardo: Boosting in Cox regression: a comparison between the likelihood-based and the model-based approaches with focus on the R-packages \textitCoxBoostand \textitmboost (2016)
  16. Dubossarsky, E.; Friedman, J. H.; Ormerod, J. T.; Wand, M. P.: Wavelet-based gradient boosting (2016)
  17. Hofner, Benjamin; Kneib, Thomas; Hothorn, Torsten: A unified framework of constrained regression (2016)
  18. Reulen, Holger; Kneib, Thomas: Boosting multi-state models (2016)
  19. Schauberger, Gunther; Tutz, Gerhard: Detection of differential item functioning in Rasch models by boosting techniques (2016)
  20. Sweeney, Elizabeth; Crainiceanu, Ciprian; Gertheiss, Jan: Testing differentially expressed genes in dose-response studies and with ordinal phenotypes (2016)

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