BACKFITTING AND SMOOTH BACKFITTING FOR ADDITIVE QUANTILE MODELS

被引：41

作者：

Lee, Young Kyung ^{[1
]}

Mammen, Enno ^{[2
]}

Park, Byeong U. ^{[3
]}

机构：

[1] Kangwon Natl Univ, Dept Stat, Chunchon 200701, South Korea

[2] Univ Mannheim, Dept Econ, D-68131 Mannheim, Germany

[3] Seoul Natl Univ, Dept Stat, Seoul 151747, South Korea

来源：

ANNALS OF STATISTICS | 2010年 / 38卷 / 05期

关键词：

Backfitting; nonparametric regression; quantile estimation; additive models; NONPARAMETRIC-ESTIMATION; ASYMPTOTIC PROPERTIES; REGRESSION QUANTILES;

D O I：

10.1214/10-AOS808

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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.

引用

页码：2857 / 2883

页数：27

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