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
Accepted author manuscript, 387 KB, PDF document
Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
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 -