ERM Scheme for Quantile Regression

Dao-Hong Xiang

Abstract and Applied Analysis, 2013, vol. 2013, issue 1

Abstract: 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.

Date: 2013
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https://doi.org/10.1155/2013/148490

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Persistent link: https://EconPapers.repec.org/RePEc:wly:jnlaaa:v:2013:y:2013:i:1:n:148490

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