Name
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1080/03610918.2015.1039133 | COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION |
Keywords | Field | DocType |
Composite quantile regression,Kernel,Nonparametric estimation,Regularization,Ridge regression | Econometrics,Principal component regression,Polynomial regression,Nonparametric regression,Local regression,Quantile,Statistics,Kernel regression,Mathematics,Quantile regression,Kernel (statistics) | Journal |
Volume | Issue | ISSN |
46 | 3 | 0361-0918 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sungwan Bang | 1 | 14 | 2.89 |
| Soo-Heang Eo | 2 | 0 | 0.34 |
| Myoungshic Jhun | 3 | 27 | 6.75 |
| Hyungjun Cho | 4 | 104 | 8.44 |