We contribute to recent research on distance correlation by extending its capability to test for
independence between two time series. The proposed test is a Portmanteau-type test based …

In the era of big data, there are many data sets recorded in equal intervals of time. To model
the change rate of such data, one often constructs a nonparametric regression model and …

Abstract The Hilbert-Schmidt Independence Criterion (HSIC) is a powerful kernel-based
statistic for assessing the generalized dependence between two multivariate variables …

Artificial Intelligence and Causal Inference address the recent development of relationships
between artificial intelligence (AI) and causal inference. Despite significant progress in AI, a …

In many statistical modeling frameworks, goodness-of-fit tests are typically administered to
the estimated residuals. In the time series setting, whiteness of the residuals is assessed …

In this paper we take a different look on the problem of testing the independence of two
infinite dimensional random variables using the distance correlation. Instead of testing if the …

Identifying how dependence relationships vary across different conditions plays a significant
role in many scientific investigations. For example, it is important for the comparison of …

Distance correlation (dCorr) is a test statistic that can identify non-linear dependence
patterns between random variables. Variations of dCorr have been applied in sequences of …

The human microbiome is an integral component of the human body. High-throughput
sequencing techniques have provided detailed information on abundance and phylogeny of …

We apply the concept of distance covariance for testing independence of two long-range
dependent time series. As test statistic we propose a linear combination of empirical …