ERM Scheme for Quantile Regression

ERM Scheme for Quantile Regression

This paper considers the ERM scheme for quantile regression. We conduct error analysis for this learning algorithm by means of a variance-expectation bound when a noise condition is satisfied for the underlying probability measure. The learning rates are derived by applying concentration techniques...

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Main Author: Dao-Hong Xiang
Format: Article
Language:English
Published: Hindawi Limited 2013-01-01
Series:Abstract and Applied Analysis
Online Access:http://dx.doi.org/10.1155/2013/148490
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spelling doaj-27538a5a738a4179a91945bcf123f9a82020-11-24T23:47:38ZengHindawi LimitedAbstract and Applied Analysis1085-33751687-04092013-01-01201310.1155/2013/148490148490ERM Scheme for Quantile RegressionDao-Hong Xiang0Department of Mathematics, Zhejiang Normal University, Jinhua, Zhejiang 321004, ChinaThis paper considers the ERM scheme for quantile regression. We conduct error analysis for this learning algorithm by means of a variance-expectation bound when a noise condition is satisfied for the underlying probability measure. The learning rates are derived by applying concentration techniques involving the ℓ2-empirical covering numbers.http://dx.doi.org/10.1155/2013/148490
collection DOAJ
language English
format Article
sources DOAJ
author Dao-Hong Xiang
spellingShingle Dao-Hong Xiang
ERM Scheme for Quantile Regression
Abstract and Applied Analysis
author_facet Dao-Hong Xiang
author_sort Dao-Hong Xiang
title ERM Scheme for Quantile Regression
title_short ERM Scheme for Quantile Regression
title_full ERM Scheme for Quantile Regression
title_fullStr ERM Scheme for Quantile Regression
title_full_unstemmed ERM Scheme for Quantile Regression
title_sort erm scheme for quantile regression
publisher Hindawi Limited
series Abstract and Applied Analysis
issn 1085-3375
1687-0409
publishDate 2013-01-01
description This paper considers the ERM scheme for quantile regression. We conduct error analysis for this learning algorithm by means of a variance-expectation bound when a noise condition is satisfied for the underlying probability measure. The learning rates are derived by applying concentration techniques involving the ℓ2-empirical covering numbers.
url http://dx.doi.org/10.1155/2013/148490
work_keys_str_mv AT daohongxiang ermschemeforquantileregression
_version_ 1725488787076677632

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