An Updated Literature Review of Distance Correlation and Its Applications to Time Series

被引：33

作者：

Edelmann, Dominic ^{[1
]}

Fokianos, Konstantinos ^{[2
]}

Pitsillou, Maria ^{[3
]}

机构：

[1] German Canc Res Ctr, Dept Biostat, Heidelberg, Germany

[2] Univ Lancaster, Dept Math & Stat, Lancaster, England

[3] Univ Cyprus, Dept Math & Stat, Nicosia, Cyprus

来源：

INTERNATIONAL STATISTICAL REVIEW | 2019年 / 87卷 / 02期

关键词：

characteristic function; distance covariance; non-linear time series; Portmanteau test statistics; spectral density; LOCAL GAUSSIAN CORRELATION; NONPARAMETRIC TEST; VARIABLE SELECTION; INDEPENDENCE; DEPENDENCE; COVARIANCE; TESTS; AUTOCORRELATIONS; STATISTICS; REGRESSION;

D O I：

10.1111/insr.12294

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

The concept of distance covariance/correlation was introduced recently to characterise dependence among vectors of random variables. We review some statistical aspects of distance covariance/correlation function, and we demonstrate its applicability to time series analysis. We will see that the auto-distance covariance/correlation function is able to identify non-linear relationships and can be employed for testing the i.i.d. hypothesis. Comparisons with other measures of dependence are included.

引用

页码：237 / 262

页数：26

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