A novel positive dependence property and its impact on a popular class of concordance measures

IF 1.4 3区 数学 Q2 STATISTICS & PROBABILITY
Sebastian Fuchs, Marco Tschimpke
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引用次数: 0

Abstract

A novel positive dependence property is introduced, called positive measure inducing (PMI for short), being fulfilled by numerous copula classes, including Gaussian, Student t, Fréchet, Farlie–Gumbel–Morgenstern and Frank copulas; it is conjectured that even all positive quadrant dependent Archimedean copulas meet this property. From a geometric viewpoint, a PMI copula concentrates more mass near the main diagonal than in the opposite diagonal. A striking feature of PMI copulas is that they impose an ordering on a certain class of copula-induced measures of concordance, the latter originating in Edwards et al. (2004) and including Spearman’s rho ρ and Gini’s gamma γ, leading to numerous new inequalities such as 3γ2ρ. The measures of concordance within this class are estimated using (classical) empirical copulas and the intrinsic construction via empirical checkerboard copulas, and the estimators’ asymptotic behavior is determined. Building upon the presented inequalities, asymptotic tests are constructed having the potential of being used for detecting whether the underlying dependence structure of a given sample is PMI, which in turn can be used for excluding certain copula families from model building. The excellent performance of the tests is demonstrated in a simulation study and by means of a real-data example.

一个新的正相关性质及其对一类流行的一致性测度的影响
引入了一种新的正相关性质,称为正测度诱导(PMI),它被许多联结函数类所满足,包括Gaussian、Student t、fr、Farlie-Gumbel-Morgenstern和Frank联结函数;我们推测,甚至所有正象限相关的阿基米德连都满足这个性质。从几何角度来看,PMI联结体在主对角线附近比在相反对角线上集中更多的质量。PMI copula的一个显著特征是,它们对某一类copula诱导的一致性度量施加了排序,后者起源于Edwards等人(2004),包括Spearman的ρ和Gini的γ γ,导致了许多新的不等式,如3γ≥2ρ。利用(经典)经验copuls和经验棋盘copuls的固有构造估计了该类内的一致性测度,并确定了估计量的渐近性。基于所提出的不等式,构造渐近检验具有用于检测给定样本的潜在依赖结构是否为PMI的潜力,这反过来可用于从模型构建中排除某些联结族。通过仿真研究和实例验证了该方法的良好性能。
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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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