Quantile regression with an epsilon-insensitive loss in a reproducing kernel Hilbert space

Jinho Park and Jeankyung Kim

Statistics & Probability Letters, 2011, vol. 81, issue 1, 62-70

Abstract: This paper proposes a method to estimate the conditional quantile function using an epsilon-insensitive loss in a reproducing kernel Hilbert space. When choosing a smoothing parameter in nonparametric frameworks, it is necessary to evaluate the complexity of the model. In this regard, we provide a simple formula for computing an effective number of parameters when implementing an epsilon-insensitive loss. We also investigate the effects of the epsilon-insensitive loss.

Keywords: Epsilon-insensitive; loss; Quantile; regression; Reproducing; kernel; Hilbert; space (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (1)

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