A METHOD TO ESTIMATE THE STATISTICAL SIGNIFICANCE OF A CORRELATION WHEN THE DATA ARE SERIALLY CORRELATED

When analyzing pairs of time series, one often needs to know whether a correlation is statistically significant. If the data are Gaussian di stributed and not serially correlated, one can use the results of clas sical statistics to estimate the significance. While some techniques c an handle non-Gaussian distributions, few methods are available for da ta with nonzero autocorrelation (i.e., serially correlated). In this p aper, a nonparametric method is suggested to estimate the statistical significance of a computed correlation coefficient when serial correla tion is a concern. This method compares favorably with conventional-me thods.