Local linear double and asymmetric kernel estimation of conditional quantiles

被引：1

作者：

Knefati, Muhammad Anas ^{[1
]}

Oulidi, Abderrahim ^{[2
]}

Abdous, Belkacem ^{[3
]}

机构：

[1] Univ Poitiers, Fac Sci, Dept Math, Poitiers, France

[2] Rabat Int Univ, Dept Stat & Actuarial, Rabat, Morocco

[3] Univ Laval, Dept Prevent & Social Med, Quebec City, PQ, Canada

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2016年 / 45卷 / 12期

关键词：

Asymmetric kernels; Beta kernels; Double kernel conditional quantile estimation; Gamma kernels; Quantile regression; 62G08; 62G05; and; 62G20; NONPARAMETRIC-ESTIMATION; REGRESSION QUANTILES; SMOOTHERS; CURVES;

D O I：

10.1080/03610926.2014.889923

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 work, we propose and investigate a family of non parametric quantile regression estimates. The proposed estimates combine local linear fitting and double kernel approaches. More precisely, we use a Beta kernel when covariate's support is compact and Gamma kernel for left-bounded supports. Finite sample properties together with asymptotic behavior of the proposed estimators are presented. It is also shown that these estimates enjoy the property of having finite variance and resistance to sparse design.

引用

页码：3473 / 3488

页数：16

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