Comment: Boosting algorithms: regularization, prediction and model fitting

DOI10.1214/07-STS242AzbMath1246.62167arXiv0804.2770OpenAlexW3103431624MaRDI QIDQ449784

Trevor Hastie

Publication date: 1 September 2012

Published in: Statistical Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0804.2770

Mathematics Subject Classification ID

Software, source code, etc. for problems pertaining to statistics (62-04) Generalized linear models (logistic models) (62J12) Estimation in survival analysis and censored data (62N02)

Related Items

Wavelet-based gradient boosting, De-noising boosting methods for variable selection and estimation subject to error-prone variables, Boosting Distributional Copula Regression, Characterizing \(L_{2}\)Boosting, Variable selection and model choice in structured survival models, Model-based boosting in R: a hands-on tutorial using the R package mboost, Nonparametric estimation of the link function including variable selection, Detection of differential item functioning in Rasch models by boosting techniques

Uses Software

• ElemStatLearn

Cites Work

• Least angle regression. (With discussion)
• Forward stagewise regression and the monotone lasso
• On the ``degrees of freedom of the lasso
• DASSO: Connections Between the Dantzig Selector and Lasso
• The elements of statistical learning. Data mining, inference, and prediction