Moderate deviations for quantile regression processes

被引：1

作者：

Mao, Mingzhi ^{[1
]}

Guo, Wanli ^{[1
]}

机构：

[1] China Univ Geosci, Sch Math & Phys, Wuhan 430074, Hubei, Peoples R China

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2019年 / 48卷 / 12期

基金：

中国国家自然科学基金;

关键词：

Moderate deviation; quantile regression; approach of argmins; exponential tightness; ASYMPTOTICS;

D O I：

10.1080/03610926.2018.1473429

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：

This paper mainly discusses the asymptotic properties of quantile regression processes. In view of the exponential tightness and convexity argument, we prove the quantile regression estimators satisfy the functional moderate deviation principle. This method can be extended to a fair range of different statistical estimation problems such as quantile regression estimators with bridge penalized functions.

引用

页码：2879 / 2892

页数：14

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