On the weak convergence and Central Limit Theorem of blurring and nonblurring processes with application to robust location estimation

被引：1

作者：

Chen, Ting-Li ^{[1
]}

Fujisawa, Hironori ^{[2
]}

Huang, Su-Yun ^{[1
]}

Hwang, Chii-Ruey ^{[3
]}

机构：

[1] Acad Sinica, Inst Stat Sci, Taipei, Taiwan

[2] Inst Stat Math, Tokyo, Japan

[3] Acad Sinica, Inst Math, Taipei, Taiwan

来源：

JOURNAL OF MULTIVARIATE ANALYSIS | 2016年 / 143卷

关键词：

Weak convergence; Central Limit Theorem; Blurring process; Robust estimation; MEAN-SHIFT; ALGORITHM;

D O I：

10.1016/j.jmva.2015.09.009

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 article studies the weak convergence and associated Central Limit Theorem for blurring and nonblurring processes. Then, they are applied to the estimation of location parameter. Simulation studies show that the location estimation based on the convergence point of blurring process is more robust and often more efficient than that of nonblurring process. (C) 2015 Elsevier Inc. All rights reserved.

引用

页码：165 / 184

页数：20

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ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS, 2015, 67 (01) :157-176

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