Statistical Science

Rejoinder: Boosting Algorithms: Regularization, Prediction and Model Fitting

Peter Bühlmann and Torsten Hothorn

Full-text: Open access

Article information

Source
Statist. Sci., Volume 22, Number 4 (2007), 516-522.

Dates
First available in Project Euclid: 7 April 2008

Permanent link to this document
https://projecteuclid.org/euclid.ss/1207580166

Digital Object Identifier
doi:10.1214/07-STS242REJ

Mathematical Reviews number (MathSciNet)
MR2420457

Zentralblatt MATH identifier
1246.62164

Citation

Bühlmann, Peter; Hothorn, Torsten. Rejoinder: Boosting Algorithms: Regularization, Prediction and Model Fitting. Statist. Sci. 22 (2007), no. 4, 516--522. doi:10.1214/07-STS242REJ. https://projecteuclid.org/euclid.ss/1207580166


Export citation

References

  • [1] Breiman, L. (2001). Random forests. Machine Learning 45 5–32.
  • [2] Bühlmann, P. and Yu, B. (2000). Discussion of “Additive logistic regression: A statistical view,” by J. Friedman, T. Hastie and R. Tibshirani. Ann. Statist. 28 377–386.
  • [3] Bühlmann, P. and Yu, B. (2008). Discussion of “Evidence contrary to the statistical view of boosting,” by D. Mease and A. Wyner. J. Machine Learning Research 9 187–194.
  • [4] Dettling, M. (2004). BagBoosting for tumor classification with gene expression data. Bioinformatics 20 3583–3593.
  • [5] Efron, B., Hastie, T., Johnstone, I. and Tibshirani, R. (2004). Least angle regression (with discussion). Ann. Statist. 32 407–499.
  • [6] Friedman, J. (2001). Greedy function approximation: A gradient boosting machine. Ann. Statist. 29 1189–1232.
  • [7] Friedman, J., Hastie, T. and Tibshirani, R. (2000). Additive logistic regression: A statistical view of boosting (with discussion). Ann. Statist. 28 337–407.
  • [8] Friedman, J. H. (2002). Stochastic gradient boosting. Comput. Statist. Data Anal. 38 367–378.
  • [9] Hothorn, T., Bühlmann, P., Kneib, T. and Schmid, M. (2007). Mboost: Model-based boosting. R package version 1.0-0. Available at http://CRAN.R-project.org.
  • [10] Künsch, H.-R., Stefanski, L. A. and Carroll, R. J. (1989). Conditionally unbiased bounded-influence estimation in general regression models, with applications to generalized linear models. J. Amer. Statist. Assoc. 84 460–466.
  • [11] Mease, D. and Wyner, A. (2008). Evidence contrary to the statistical view of boosting. J. Machine Learning Research 9 131–156.