Spectral Inference under Complex Temporal Dynamics
复杂时间动态下的谱推断AI 解读 ↓
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AI 根据所提供的论文材料归类:摘要明确研究局部平稳时间过程的谱密度推断及白噪声、平稳性等时间序列检验。
复杂时间动态下的谱推断AI 解读 ↓
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We develop a unified theory and methodology for the inference of evolutionary Fourier power spectra for a general class of locally stationary and possibly nonlinear processes. In particular, simultaneous confidence regions (SCR) with asymptotically correct coverage rates are constructed for the evolutionary spectral densities on a nearly optimally dense grid of the joint time-frequency domain. A simulation based bootstrap method is proposed to implement the SCR. The SCR enables researchers and practitioners to visually evaluate the magnitude and pattern of the evolutionary power spectra with asymptotically accurate statistical guarantee. The SCR also serves as a unified tool for a wide range of statistical inference problems in time-frequency analysis ranging from tests for white noise, stationarity and time-frequency separability to the validation for non-stationary linear models.