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    Rights statement: This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Biometrika following peer review. The definitive publisher-authenticated version K Fokianos, M Pitsillou; Testing independence for multivariate time series via the auto-distance correlation matrix, Biometrika, Volume 105, Issue 2, 1 June 2018, Pages 337–352, https://doi.org/10.1093/biomet/asx082

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Testing independence for multivariate time series via the auto-distance correlation matrix

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Testing independence for multivariate time series via the auto-distance correlation matrix. / Fokianos, K.; Pitsillou, M.
In: Biometrika, Vol. 105, No. 2, 01.06.2018, p. 337-352.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Fokianos, K & Pitsillou, M 2018, 'Testing independence for multivariate time series via the auto-distance correlation matrix', Biometrika, vol. 105, no. 2, pp. 337-352. https://doi.org/10.1093/biomet/asx082

APA

Fokianos, K., & Pitsillou, M. (2018). Testing independence for multivariate time series via the auto-distance correlation matrix. Biometrika, 105(2), 337-352. https://doi.org/10.1093/biomet/asx082

Vancouver

Fokianos K, Pitsillou M. Testing independence for multivariate time series via the auto-distance correlation matrix. Biometrika. 2018 Jun 1;105(2):337-352. Epub 2018 Jan 20. doi: 10.1093/biomet/asx082

Author

Fokianos, K. ; Pitsillou, M. / Testing independence for multivariate time series via the auto-distance correlation matrix. In: Biometrika. 2018 ; Vol. 105, No. 2. pp. 337-352.

Bibtex

@article{e349d942d87c4db899f2561541177542,
title = "Testing independence for multivariate time series via the auto-distance correlation matrix",
abstract = "We introduce the matrix multivariate auto-distance covariance and correlation functions for time series, discuss their interpretation and develop consistent estimators for practical implementation. We also develop a test of the independent and identically distributed hypothesis for multivariate time series data and show that it performs better than the multivariate Ljung–Box test. We discuss computational aspects and present a data example to illustrate the method.",
author = "K. Fokianos and M. Pitsillou",
note = "This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Biometrika following peer review. The definitive publisher-authenticated version K Fokianos, M Pitsillou; Testing independence for multivariate time series via the auto-distance correlation matrix, Biometrika, Volume 105, Issue 2, 1 June 2018, Pages 337–352, https://doi.org/10.1093/biomet/asx082 ",
year = "2018",
month = jun,
day = "1",
doi = "10.1093/biomet/asx082",
language = "English",
volume = "105",
pages = "337--352",
journal = "Biometrika",
issn = "0006-3444",
publisher = "Oxford University Press",
number = "2",

}

RIS

TY - JOUR

T1 - Testing independence for multivariate time series via the auto-distance correlation matrix

AU - Fokianos, K.

AU - Pitsillou, M.

N1 - This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Biometrika following peer review. The definitive publisher-authenticated version K Fokianos, M Pitsillou; Testing independence for multivariate time series via the auto-distance correlation matrix, Biometrika, Volume 105, Issue 2, 1 June 2018, Pages 337–352, https://doi.org/10.1093/biomet/asx082

PY - 2018/6/1

Y1 - 2018/6/1

N2 - We introduce the matrix multivariate auto-distance covariance and correlation functions for time series, discuss their interpretation and develop consistent estimators for practical implementation. We also develop a test of the independent and identically distributed hypothesis for multivariate time series data and show that it performs better than the multivariate Ljung–Box test. We discuss computational aspects and present a data example to illustrate the method.

AB - We introduce the matrix multivariate auto-distance covariance and correlation functions for time series, discuss their interpretation and develop consistent estimators for practical implementation. We also develop a test of the independent and identically distributed hypothesis for multivariate time series data and show that it performs better than the multivariate Ljung–Box test. We discuss computational aspects and present a data example to illustrate the method.

U2 - 10.1093/biomet/asx082

DO - 10.1093/biomet/asx082

M3 - Journal article

VL - 105

SP - 337

EP - 352

JO - Biometrika

JF - Biometrika

SN - 0006-3444

IS - 2

ER -