基于核归一化交叉协方差算子的函数变量独立性检验

IF 1.4 3区 数学 Q2 STATISTICS & PROBABILITY
Terence Kevin Manfoumbi Djonguet, Guy Martial Nkiet
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引用次数: 0

摘要

我们利用对归一化交叉协方差算子的通常经验估计值的希尔伯特-施密特平方规范进行适当居中和重定标后得到的检验统计量,提出了一种对计量空间中随机变量进行估值的独立性检验方法。然后,我们得到了该统计量在独立性假设下的渐近正态性,从而得出了一种新的函数式随机变量独立性检验方法。我们还提供了一项模拟研究,可以将提出的检验与现有的检验进行比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An independence test for functional variables based on kernel normalized cross-covariance operator

We propose an independence test for random variables valued into metric spaces by using a test statistic obtained from appropriately centering and rescaling the squared Hilbert–Schmidt norm of the usual empirical estimator of normalized cross-covariance operator. We then get asymptotic normality of this statistic under independence hypothesis, so leading to a new test for independence of functional random variables. A simulation study that allows to compare the proposed test to existing ones is provided.

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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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