渐近预期灵敏度函数及其在单调关联测量中的应用

IF 0.8 4区 数学 Q3 STATISTICS & PROBABILITY Annals of the Institute of Statistical Mathematics Pub Date : 2024-08-17 DOI:10.1007/s10463-024-00910-z
Qingyang Zhang
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引用次数: 0

摘要

我们引入了一种新型的影响函数--渐近预期灵敏度函数,它通常等同于传统的基于 Gâteaux 导数的影响函数,但在数学上比它更容易理解。为了说明这一点,我们研究了一些重要关联测量的稳健性,包括斯皮尔曼等级相关性和肯德尔一致性测量,以及最近开发的查特吉相关性。
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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Asymptotic expected sensitivity function and its applications to measures of monotone association

We introduce a new type of influence function, the asymptotic expected sensitivity function, which is often equivalent to but mathematically more tractable than the traditional one based on the Gâteaux derivative. To illustrate, we study the robustness of some important measures of association, including Spearman’s rank correlation and Kendall’s concordance measure, and the recently developed Chatterjee’s correlation.

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来源期刊
CiteScore
2.00
自引率
0.00%
发文量
39
审稿时长
6-12 weeks
期刊介绍: Annals of the Institute of Statistical Mathematics (AISM) aims to provide a forum for open communication among statisticians, and to contribute to the advancement of statistics as a science to enable humans to handle information in order to cope with uncertainties. It publishes high-quality papers that shed new light on the theoretical, computational and/or methodological aspects of statistical science. Emphasis is placed on (a) development of new methodologies motivated by real data, (b) development of unifying theories, and (c) analysis and improvement of existing methodologies and theories.
期刊最新文献
Estimation of value-at-risk by $$L^{p}$$ quantile regression Simplified quasi-likelihood analysis for a locally asymptotically quadratic random field Asymptotic expected sensitivity function and its applications to measures of monotone association Penalized estimation for non-identifiable models Hidden AR process and adaptive Kalman filter
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