The Annals of Statistics

Backfitting and smooth backfitting for additive quantile models

Young Kyung Lee, Enno Mammen, and Byeong U. Park

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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.

Article information

Source
Ann. Statist., Volume 38, Number 5 (2010), 2857-2883.

Dates
First available in Project Euclid: 20 July 2010

Permanent link to this document
https://projecteuclid.org/euclid.aos/1279638542

Digital Object Identifier
doi:10.1214/10-AOS808

Mathematical Reviews number (MathSciNet)
MR2722458

Zentralblatt MATH identifier
1200.62039

Subjects
Primary: 62G08: Nonparametric regression
Secondary: 62G20: Asymptotic properties

Keywords
Backfitting nonparametric regression quantile estimation additive models

Citation

Lee, Young Kyung; Mammen, Enno; Park, Byeong U. Backfitting and smooth backfitting for additive quantile models. Ann. Statist. 38 (2010), no. 5, 2857--2883. doi:10.1214/10-AOS808. https://projecteuclid.org/euclid.aos/1279638542


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