自举绝对偏差中值的巴哈杜尔表示法及其在投影深度加权平均值中的应用

IF 0.9 4区数学 Q3 STATISTICS & PROBABILITY

Metrika Pub Date : 2024-03-19 DOI:10.1007/s00184-024-00958-0

Qing Liu, Xiaohui Liu, Zihao Hu

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引用次数: 0

摘要

中位绝对偏差（以下简称 MAD）被称为普通方差的稳健替代方案。它被广泛用于稳健的统计推断程序。在本文中，我们研究了其 bootstrap 对应的强和弱 Bahadur 表示。作为一个有用的应用，我们利用这些结果推导出了自举样本投影深度加权均值的弱巴哈多表示--这是一个依赖于 MAD 的相当重要的位置估计器。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Bahadur representations for the bootstrap median absolute deviation and the application to projection depth weighted mean

Median absolute deviation (hereafter MAD) is known as a robust alternative to the ordinary variance. It has been widely utilized to induce robust statistical inferential procedures. In this paper, we investigate the strong and weak Bahadur representations of its bootstrap counterpart. As a useful application, we utilize the results to derive the weak Bahadur representation of the bootstrap sample projection depth weighted mean—a quite important location estimator depending on MAD.

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来源期刊

Metrika 数学-统计学与概率论

CiteScore

1.50

自引率

14.30%

发文量

审稿时长

6-12 weeks

期刊介绍： Metrika is an international journal for theoretical and applied statistics. Metrika publishes original research papers in the field of mathematical statistics and statistical methods. Great importance is attached to new developments in theoretical statistics, statistical modeling and to actual innovative applicability of the proposed statistical methods and results. Topics of interest include, without being limited to, multivariate analysis, high dimensional statistics and nonparametric statistics; categorical data analysis and latent variable models; reliability, lifetime data analysis and statistics in engineering sciences.