Dimension reduction boosting |
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Authors: | Junlong Zhao Xiuli Zhao |
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Affiliation: | 1. School of Statistics, Beijing Normal University, Beijing, China;2. Ant Financial Services Group, Hangzhou, China |
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Abstract: | L2Boosting is an effective method for constructing model. In the case of high-dimensional setting, Bühlmann and Yu (2003 Bühlmann, P., Yu, B. (2003). Boosting with the L2-loss: regression and classification. J. Amer. Stat. Assoc. 98:324–339.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) 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. |
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Keywords: | Conjugate direction boosting dimension reduction L2Boosting sliced inverse regression |
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