Composite kernel quantile regression |
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Authors: | Sungwan Bang Soo-Heang Eo Myoungshic Jhun |
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Affiliation: | 1. Department of Mathematics, Korea Military Academy, Seoul, Republic of Korea;2. Department of Statistics, Korea University, Seoul, Republic of Korea |
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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. |
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Keywords: | Composite quantile regression Kernel Nonparametric estimation Regularization Ridge regression |
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