期刊
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
卷 102, 期 477, 页码 255-268出版社
TAYLOR & FRANCIS INC
DOI: 10.1198/016214506000000979
关键词
degrees of freedom; metric entropy; model selection; quadratic programming; quantile regression; reproducing kernel; Hilbert space
In this article we consider quantile regression in reproducing kernel Hilbert spaces, which we call kernel quantile regression (KQR). We make three contributions: (1) we propose an efficient algorithm that computes the entire solution path of the KQR, with essentially the same computational cost as fitting one KQR model; (2) we derive a simple formula for the effective dimension of the KQR model, which allows convenient selection of the regularization parameter; and (3) we develop an asymptotic theory for the KQR model.
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