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Composite kernel quantile regression

Authors
Bang, SungwanEo, Soo-HeangJhun, MyoungshicCho, Hyung Jun
Issue Date
2017
Publisher
TAYLOR & FRANCIS INC
Keywords
Composite quantile regression; Kernel; Nonparametric estimation; Regularization; Ridge regression
Citation
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, v.46, no.3, pp.2228 - 2240
Indexed
SCIE
SCOPUS
Journal Title
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
Volume
46
Number
3
Start Page
2228
End Page
2240
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/86282
DOI
10.1080/03610918.2015.1039133
ISSN
0361-0918
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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정경대학 (통계학과)
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