期刊
ELECTRONIC JOURNAL OF STATISTICS
卷 10, 期 1, 页码 783-805出版社
INST MATHEMATICAL STATISTICS
DOI: 10.1214/16-EJS1122
关键词
Inverse regression; L2 support vector machine; Reproducing kernel Hilbert space
Principal support vector machine was proposed recently by Li, Artemiou and Li (2011) to combine L1 support vector machine and sufficient dimension reduction. We introduce the principal Lq support vector machine as a unified framework for linear and nonlinear sufficient dimension reduction. By noticing that the solution of L1 support vector machine may not be unique, we set q > 1 to ensure the uniqueness of the solution. The asymptotic distribution of the proposed estimators are derived for q > 1. We demonstrate through numerical studies that the proposed L2 support vector machine estimators improve existing methods in accuracy, and are less sensitive to the tuning parameter selection.
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