Quantile Regression in Reproducing Kernel Hilbert Spaces
نویسندگان
چکیده
In this paper we consider quantile regression in reproducing kernel Hilbert spaces, which we refer to as 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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