Boosting Algorithms: Regularization, Prediction and Model Fitting

Details Boosting Algorithms: Regularization, Prediction and Model Fitting Boosting Algorithms: Regularization, Prediction and Model Fitting

2008-04-17

arxiv.org/abs/0804.2752v1

Statistical Science 2007, Vol. 22, No. 4, 477-505

Statistics

Methodology

This paper commented in: [arXiv:0804.2757], [arXiv:0804.2770]. Rejoinder in [arXiv:0804.2777]. Published in at http://dx.doi

Scientific paper

10.1214/07-STS242

We present a statistical perspective on boosting. Special emphasis is given to estimating potentially complex parametric or nonparametric models, including generalized linear and additive models as well as regression models for survival analysis. Concepts of degrees of freedom and corresponding Akaike or Bayesian information criteria, particularly useful for regularization and variable selection in high-dimensional covariate spaces, are discussed as well. The practical aspects of boosting procedures for fitting statistical models are illustrated by means of the dedicated open-source software package mboost. This package implements functions which can be used for model fitting, prediction and variable selection. It is flexible, allowing for the implementation of new boosting algorithms optimizing user-specified loss functions.

Affiliated with

Also associated with

No associations

Rating

Boosting Algorithms: Regularization, Prediction and Model Fitting does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Boosting Algorithms: Regularization, Prediction and Model Fitting, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Boosting Algorithms: Regularization, Prediction and Model Fitting will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-506653