Complex-valued representation of a two-component real-valued time series yields additional physical insights that are lost otherwise. The spectral representation theorem allows us to study covariance stationary complex-valued random sequences in the frequency domain, and this is known as rotary spectral analysis. It is a widely-used technique for studying elliptical motions in ocean currents, wind etc. An important and useful parameter in rotary spectral analysis of scalar complex-valued time series is the rotary coefficient. It measures the tendency of vectors to rotate in a clockwise or counter-clockwise manner. We derive the theoretical distribution of the rotary coefficient estimator and apply our results to ocean current speed and direction measurements at six depths in the Labrador Sea.
Canonical correlation techniques are commonly employed in the analysis of a pair of
vector-valued random variables. We introduce a framework to extend classical multivariate analysis techniques such as canonical correlation analysis, partial least squares, and multivariate linear regression, to define coherence – a measure of correlation in the frequency domain. In the statistical analysis of complex-valued time series, we refer to a time series as proper/improper according to whether it is uncorrelated/correlated with its complex conjugate. In earlier work, complex-valued signals were assumed to be proper for the simple reason that it led to a simpler algebra. However, the loss in performance caused by overlooking the potential impropriety of such data is realized to be significant, and therefore, when the data is improper, information contained in the complementary covariance structure must be considered. Since impropriety in the time domain may not necessarily correspond to impropriety at all frequencies, we propose a generalized likelihood ratio test which may be used to test propriety of a discrete time complex-valued process at a given frequency. Finally, the idea of vector circulant embedding is exploited to yield a frequency domain
bootstrap methodology. With the help of three example parameters involved in the study of
multi-channel complex-valued time series, we illustrate how our method allows us to draw
statistical inference such as confidence intervals. Our method can prove useful in cases
where no theoretical distributional results are available, or to check the effect of nuisance
parameter estimates where theoretical results are available.