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October 2010 Backfitting and smooth backfitting for additive quantile models
Young Kyung Lee, Enno Mammen, Byeong U. Park
Ann. Statist. 38(5): 2857-2883 (October 2010). DOI: 10.1214/10-AOS808

Abstract

In this paper, we study the ordinary backfitting and smooth backfitting as methods of fitting additive quantile models. We show that these backfitting quantile estimators are asymptotically equivalent to the corresponding backfitting estimators of the additive components in a specially-designed additive mean regression model. This implies that the theoretical properties of the backfitting quantile estimators are not unlike those of backfitting mean regression estimators. We also assess the finite sample properties of the two backfitting quantile estimators.

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Young Kyung Lee. Enno Mammen. Byeong U. Park. "Backfitting and smooth backfitting for additive quantile models." Ann. Statist. 38 (5) 2857 - 2883, October 2010. https://doi.org/10.1214/10-AOS808

Information

Published: October 2010
First available in Project Euclid: 20 July 2010

zbMATH: 1200.62039
MathSciNet: MR2722458
Digital Object Identifier: 10.1214/10-AOS808

Subjects:
Primary: 62G08
Secondary: 62G20

Rights: Copyright © 2010 Institute of Mathematical Statistics

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Vol.38 • No. 5 • October 2010
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