Rights statement: This is the peer reviewed version of the following article: Edelmann, D., Fokianos, K., and Pitsillou, M. ( 2019) An Updated Literature Review of Distance Correlation and Its Applications to Time Series. International Statistical Review, 87: 237– 262. https://doi.org/10.1111/insr.12294 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/insr.12294/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
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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 - An Updated Literature Review of Distance Correlation and Its Applications to Time Series
AU - Edelmann, Dominic
AU - Fokianos, Konstantinos
AU - Pitsillou, Maria
N1 - This is the peer reviewed version of the following article: Edelmann, D., Fokianos, K., and Pitsillou, M. ( 2019) An Updated Literature Review of Distance Correlation and Its Applications to Time Series. International Statistical Review, 87: 237– 262. https://doi.org/10.1111/insr.12294 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/insr.12294/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
PY - 2019/8/1
Y1 - 2019/8/1
N2 - 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.
AB - 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.
KW - characteristic function
KW - distance covariance
KW - non‐linear time series
KW - Portmanteau test statistics
KW - spectral density
U2 - 10.1111/insr.12294
DO - 10.1111/insr.12294
M3 - Journal article
VL - 87
SP - 237
EP - 262
JO - International Statistical Review
JF - International Statistical Review
SN - 0306-7734
IS - 2
ER -