Comparison of correlation-based measures of concordance in terms of asymptotic variance

IF 1.4 3区 数学 Q2 STATISTICS & PROBABILITY Journal of Multivariate Analysis Pub Date : 2023-11-24 DOI:10.1016/j.jmva.2023.105265
Takaaki Koike , Marius Hofert
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Abstract

We compare measures of concordance that arise as Pearson’s linear correlation coefficient between two random variables transformed so that they follow the so-called concordance-inducing distributions. The class of such transformed rank correlations includes Spearman’s rho, Blomqvist’s beta and van der Waerden’s coefficient. When only the standard axioms of measures of concordance are required, it is not always clear which transformed rank correlation is most suitable to use. To address this question, we compare measures of concordance in terms of their best and worst asymptotic variances of some canonical estimators over a certain set of dependence structures. A simple criterion derived from this approach is that concordance-inducing distributions with smaller fourth moment are more preferable. In particular, we show that Blomqvist’s beta is the optimal transformed rank correlation in this sense, and Spearman’s rho outperforms van der Waerden’s coefficient. Moreover, we find that Kendall’s tau, although it is not a transformed rank correlation of that nature, shares a certain optimal structure with Blomqvist’s beta.

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从渐近方差的角度比较基于相关的一致性度量
我们比较了两个随机变量之间产生的皮尔逊线性相关系数的一致性度量,使它们遵循所谓的一致性诱导分布。这类转换后的秩相关包括斯皮尔曼系数、布洛姆奎斯特系数和范德瓦尔登系数。当只需要一致性度量的标准公理时,并不总是清楚哪一种转换后的秩相关最适合使用。为了解决这个问题,我们比较了一些典型估计量在一组依赖结构上的最佳和最差渐近方差的一致性度量。从这种方法中得出的一个简单准则是,具有较小第四矩的一致性诱导分布更可取。特别是,我们表明,在这种意义上,Blomqvist的beta是最优的变换秩相关,而Spearman的rho优于van der Waerden的系数。此外,我们发现Kendall的tau虽然不是那种性质的转换等级相关,但它与Blomqvist的beta具有一定的最优结构。
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来源期刊
Journal of Multivariate Analysis
Journal of Multivariate Analysis 数学-统计学与概率论
CiteScore
2.40
自引率
25.00%
发文量
108
审稿时长
74 days
期刊介绍: Founded in 1971, the Journal of Multivariate Analysis (JMVA) is the central venue for the publication of new, relevant methodology and particularly innovative applications pertaining to the analysis and interpretation of multidimensional data. The journal welcomes contributions to all aspects of multivariate data analysis and modeling, including cluster analysis, discriminant analysis, factor analysis, and multidimensional continuous or discrete distribution theory. Topics of current interest include, but are not limited to, inferential aspects of Copula modeling Functional data analysis Graphical modeling High-dimensional data analysis Image analysis Multivariate extreme-value theory Sparse modeling Spatial statistics.
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