Dimension reduction boosting

Junlong Zhao and Xiuli Zhao

Communications in Statistics - Theory and Methods, 2017, vol. 46, issue 9, 4151-4162

Abstract: L2Boosting is an effective method for constructing model. In the case of high-dimensional setting, Bühlmann and Yu (2003) proposed the componentwise L2Boosting, but componentwise L2Boosting can only fit a special limited model. In this paper, by combining a boosting and sufficient dimension reduction method, e.g., sliced inverse regression (SIR), we propose a new method for regression, called dimension reduction boosting (DRBoosting). Compared with L2Boosting, the computation of DRBoosting is less intensive and its prediction is better, especially for high-dimensional data. Simulations confirm the advantage of the new method.

Date: 2017
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DOI: 10.1080/03610926.2015.1079637

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