A smoothing stochastic algorithm for quantile estimation

被引：4

作者：

Amiri, Aboubacar ^{[1
]}

Thiam, Baba ^{[1
]}

机构：

[1] Univ Lille 3, Lab EQUIPPE, EA 4018, Villeneuve Dascq, France

来源：

STATISTICS & PROBABILITY LETTERS | 2014年 / 93卷

关键词：

Quantile estimation; Stochastic approximation; Nonparametric estimation; Almost-sure convergence; Asymptotic normality; APPROXIMATION;

D O I：

10.1016/j.spl.2014.06.016

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 provide the almost-sure convergence and the asymptotic normality of a smooth version of the Robbins-Monro algorithm for the quantile estimation. A Monte Carlo simulation study shows that our proposed method works well within the framework of a data stream. (C) 2014 Elsevier B.V. All rights reserved.

引用

页码：116 / 125

页数：10

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