The paper presents a statistical perspective on boosting, emphasizing its use in estimating complex parametric or nonparametric models, including generalized linear and additive models, as well as regression models for survival analysis. It discusses concepts like degrees of freedom and information criteria for regularization and variable selection in high-dimensional settings. The R package mboost is introduced, offering tools for model fitting, prediction, and variable selection, with flexibility for new boosting algorithms. The paper reviews the theoretical and practical aspects of boosting, highlighting its connection to statistical estimation and additive modeling. It discusses the AdaBoost algorithm, its properties, and the slow overfitting behavior of boosting. The paper also explores various loss functions and boosting algorithms, including $ L_2 $-Boosting, BinomialBoosting, and others, and their applications in regression, classification, and survival analysis. It emphasizes the importance of choosing appropriate base procedures and tuning parameters for effective model fitting. The paper concludes with insights into the statistical properties of boosting, its relationship to functional gradient descent, and its utility in high-dimensional data analysis.The paper presents a statistical perspective on boosting, emphasizing its use in estimating complex parametric or nonparametric models, including generalized linear and additive models, as well as regression models for survival analysis. It discusses concepts like degrees of freedom and information criteria for regularization and variable selection in high-dimensional settings. The R package mboost is introduced, offering tools for model fitting, prediction, and variable selection, with flexibility for new boosting algorithms. The paper reviews the theoretical and practical aspects of boosting, highlighting its connection to statistical estimation and additive modeling. It discusses the AdaBoost algorithm, its properties, and the slow overfitting behavior of boosting. The paper also explores various loss functions and boosting algorithms, including $ L_2 $-Boosting, BinomialBoosting, and others, and their applications in regression, classification, and survival analysis. It emphasizes the importance of choosing appropriate base procedures and tuning parameters for effective model fitting. The paper concludes with insights into the statistical properties of boosting, its relationship to functional gradient descent, and its utility in high-dimensional data analysis.