Journal
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
Volume 102, Issue 477, Pages 255-268Publisher
TAYLOR & FRANCIS INC
DOI: 10.1198/016214506000000979
Keywords
degrees of freedom; metric entropy; model selection; quadratic programming; quantile regression; reproducing kernel; Hilbert space
Categories
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available