Quantile Regression with Gaussian Kernels

Baobin Wang (), Ting Hu () and Hong Yin ()
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Baobin Wang: South-Central University for Nationalities, School of Mathematics and Statistics
Ting Hu: Wuhan University, School of Mathematics and Statistics
Hong Yin: Renmin University of China, School of Mathematics

Chapter Chapter 24 in Contemporary Experimental Design, Multivariate Analysis and Data Mining, 2020, pp 373-386 from Springer

Abstract: Abstract This paper aims at the error analysis of stochastic gradient descent (SGD) for quantile regression, which is associated with a sequence of varying $$\epsilon $$-insensitive pinball loss functions and flexible Gaussian kernels. Analyzing sparsity and learning rates will be provided when the target function lies in some Sobolev spaces and a noise condition is satisfied for the underlying probability measure. Our results show that selecting the variance of the Gaussian kernel plays a crucial role in the learning performance of quantile regression algorithms.

Keywords: Quantile regresion; Gaussian kernels; Reproducing kernel Hilbert spaces; Insensitive pinball loss; Learning rate (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-46161-4_24

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DOI: 10.1007/978-3-030-46161-4_24

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