Sparse high-dimensional semi-nonparametric quantile regression in a reproducing kernel Hilbert space

Yue Wang, Yan Zhou, Rui Li and Heng Lian

Computational Statistics & Data Analysis, 2022, vol. 168, issue C

Abstract: We consider partially linear quantile regression with a high-dimensional linear part, with the nonparametric function assumed to be in a reproducing kernel Hilbert space. We establish the overall learning rate in this setting, as well as the rate of the linear part separately. Our proof relies heavily on the empirical processes and the Rademacher complexity in the semi-nonparametric setting as analytic tools. Some simulation studies and a real data analysis are presented for illustration.

Keywords: Convergence rate; Prediction risk; Quantile regression; Rademacher complexity (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:168:y:2022:i:c:s016794732100222x

DOI: 10.1016/j.csda.2021.107388

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