Asymptotic Normality of a Kernel Conditional Quantile Estimator Under Strong Mixing Hypothesis and Left-Truncation

被引：3

作者：

Said, Elias Ould ^{[1
,2
]}

Yahia, Djabrane ^{[3
]}

机构：

[1] Univ Lille Nord France, F-59000 Lille, France

[2] ULCO, LMPA, Ctr Mi Voix, Calais, France

[3] Univ Mohamed Khider, Lab Appl Math, Biskra, Algeria

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2011年 / 40卷 / 14期

关键词：

Asymptotic normality; Conditional quantile; Kernel estimate; Strong mixing; Truncated data; TIME-SERIES; REPRESENTATIONS; PROBABILITY;

D O I：

10.1080/03610926.2010.489171

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：

We consider the estimation of the conditional quantile when the interest variable is subject to left truncation. Under regularity conditions, it is shown that the kernel estimate of the conditional quantile is asymptotically normally distributed, when the data exhibit some kind of dependence. We use asymptotic normality to construct confidence bands for predictors based on the kernel estimate of the conditional median.

引用

页码：2605 / 2627

页数：23

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