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

By Dominic Edelmann, Konstantinos Fokianos, Maria Pitsillou. International Statistical Review, 2017

56 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b19e0ffe476d125cde502b517577667027b0f1b2

Citations in the corpus

• A Modified CRITIC Method to Estimate the Objective Weights of Decision Criteria (2021, unknown): “teria and not the nonlinear relationship [45,46].”