Estimation of coherence spectrum of non-Gaussian time series populations

Previous work on computation of coherence estimates between two time series and the confidence intervals about these estimates has always assumed that the time series have a Gaussian probability density function. Here a Monte Carlo study was performed, computing coherences and confidence intervals upon non-Gaussian time series. Using both a rectangular distribution and a x 2 distribution with one degree of freedom, the results appear to justify the notion that the assumption of a Gaussian distribution has a fairly small importance in the computation of the above statistics.