Backfitting and smooth backfitting in varying coefficient quantile regression

被引：5
|
作者：
Lee, Young K. [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：
ECONOMETRICS JOURNAL | 2014年 / 17卷 / 02期
基金：
新加坡国家研究基金会;
关键词：
Backfitting; Integral equation; Kernel smoothing; Quantile regression; Smooth backfitting; Varying coefficient models;
D O I：
10.1111/ectj.12017
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
In this paper, we study ordinary backfitting and smooth backfitting as methods of fitting varying coefficient quantile models. We do this in a unified framework that accommodates various types of varying coefficient models. Our framework also covers the additive quantile model as a special case. Under a set of weak conditions, we derive the asymptotic distributions of the backfitting estimators. We also briefly report on the results of a simulation study.
引用
收藏
页码：S20 / S38
页数：19
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