Distance covariance for stochastic processes

Muneya Matsui; Thomas Valentin Mikosch; Gennady Samorodnitsky

doi:10.19195/0208-4147.37.2.9

Distance covariance for stochastic processes

Muneya Matsui, Thomas Valentin Mikosch, Gennady Samorodnitsky

Department of Mathematical Sciences

2 Citations (Scopus)

14 Downloads (Pure)

Abstract

The distance covariance of two random vectors is a measure of their dependence. The empirical distance covariance and correlation can be used as statistical tools for testing whether two random vectors are independent. We propose an analog of the distance covariance for two stochastic processes defined on some interval. Their empirical analogs can be used to test the independence of two processes.

Original language	English
Journal	Probability and Mathematical Statistics
Volume	37
Issue number	2
Pages (from-to)	355-372
Number of pages	18
ISSN	0208-4147
DOIs	https://doi.org/10.19195/0208-4147.37.2.9
Publication status	Published - 2017

Keywords

Distance covariance
Empirical characteristic function
Stochastic process
Test of independence

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10.19195/0208-4147.37.2.9

Distance covariance for stochastic processesFinal published version, 152 KB

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abstract = "The distance covariance of two random vectors is a measure of their dependence. The empirical distance covariance and correlation can be used as statistical tools for testing whether two random vectors are independent. We propose an analog of the distance covariance for two stochastic processes defined on some interval. Their empirical analogs can be used to test the independence of two processes.",

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AU - Matsui, Muneya

AU - Mikosch, Thomas Valentin

AU - Samorodnitsky, Gennady

PY - 2017

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AB - The distance covariance of two random vectors is a measure of their dependence. The empirical distance covariance and correlation can be used as statistical tools for testing whether two random vectors are independent. We propose an analog of the distance covariance for two stochastic processes defined on some interval. Their empirical analogs can be used to test the independence of two processes.

KW - Distance covariance

KW - Empirical characteristic function

KW - Stochastic process

KW - Test of independence

U2 - 10.19195/0208-4147.37.2.9

DO - 10.19195/0208-4147.37.2.9

M3 - Journal article

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VL - 37

SP - 355

EP - 372

JO - Probability and Mathematical Statistics

JF - Probability and Mathematical Statistics

IS - 2

ER -

Distance covariance for stochastic processes

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Keywords

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