  Boosting is a popular algorithm in supervised machine learning with wide
applications in regression and classification problems. Boosting can combine a
sequence of regression trees to obtain accurate prediction. In the presence of
outliers, traditional boosting may show inferior results since the algorithm
optimizes a convex loss function. Recent literature has proposed boosting
algorithms to optimizing robust nonconvex loss functions. However, there is a
lack of weighted estimation to indicate the outlier status of the observations.
This article proposes an iteratively reweighted boosting algorithm combining
robust loss optimization and weighted estimation, which is conveniently
constructed with existing software. The output includes the weights as a
valuable diagnostic to the outlier status of the observations. For
practitioners interested in the boosting algorithm, the new algorithm can be
interpreted as a method to tuning in observation weights, which can lead to a
more accurate model. The R package irboost is demonstrated with publicly
available data in various robust boosting approaches to generalized linear
models, classification and survival data analysis.
