Fourier Analysis of Serial Dependence Measures
研究了用U统计量估计的替代依赖度量（如Kendall's τ）替代自协方差进行谱分析的新频域方法，并揭示了其渐近性质，包括Kendall's τ的极限方差呈现出的意外行为。
Classical spectral analysis is based on the discrete Fourier transform of the autocovariances. In this article we investigate the asymptotic properties of new frequency‐domain methods where the autocovariances in the spectral density are replaced by alternative dependence measures that can be estimated by U ‐statistics. An interesting example is given by Kendall's τ , for which the limiting variance exhibits a surprising behavior.