@Rick_SAS Thanks for the comprehensive answer!. While it may take me a while to figure out what fits my needs the best, I've got to thank you for guiding me for I know better now which way I should be headed. In a gist, I seek to assess correlation and cross-correlation between several variables, two variables at a time. The datapoints are temporal in nature. What has been observed is that the residuals of a few such variables, after compensation for trends and autocorrelation effects, possibly have non-linear correlation/cross-correlation. I only seek a measure of association that is robust to such non-linear associations; Pearson's falls short owing to it sensitivity to linear associations whereas Spearman's is not necessarily sensitive to associations that are not monotonic (e.g. quadratic). In my research (I admit not as deep as it should be), I found distance correlation to be a method that fits my requirements and has shown promising results. This especially so when I cannot visually check residuals on a case-by-case basis owing to the fact that I need to run the test across several datasets. The following research encapsulates in essence what I seek to achieve. MEASURING AND TESTING DEPENDENCE BY CORRELATION OF DISTANCES By Gabor J. Szekely,Maria L. Rizzo and Nail K. Bakirov
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