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ABSTRACT
The composite quantile regression (CQR) has been developed for the robust and efficient estimation of regression coefficients in a liner regression model. By employing the idea of the CQR, we propose a new regression method, called composite kernel quantile regression (CKQR), which uses the sum of multiple check functions as a loss in reproducing kernel Hilbert spaces for the robust estimation of a nonlinear regression function. The numerical results demonstrate the usefulness of the proposed CKQR in estimating both conditional nonlinear mean and quantile functions.
MATHEMATICAL SUBJECT CLASSIFICATION:
Acknowledgments
The authors are grateful to the editor, the associate editor, and the reviewers for their constructive and insightful comments and suggestions, which helped to dramatically improve the quality of this article. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by (1) the Ministry of Science, ICT and Future Planning (NRF-2013R1A1A1007536) for S. Bang, (2) the Ministry of Education (NRF-2013R1A1A2A10007545) for M. Jhun, and (3) the Ministry of Education, Science and Technology (2010–0007936) for H. Cho.