Spatially Adaptive Estimation of Spectrum
Abstract
When analyzing a stationary time series, one of the questions we are often interested in is how to estimate its spectrum. Many approaches have been proposed to this end. Most are focused on smoothing the periodogram using a single smoothing parameter across all Fourier frequencies. In this paper, we smooth the log periodogram by placing a spatially adaptive prior called the dynamic shrinkage prior, so that varying degrees of smoothing may be applied to different intervals of Fourier frequencies, resulting in less biased estimates of the spectrum. Further research will extend this approach to spectral estimation for nonstationary time series.
Subject Area
Statistics
Recommended Citation
Xie, Yi, "Spatially Adaptive Estimation of Spectrum" (2020). ETD Collection for University of Texas, El Paso. AAI28261130.
https://scholarworks.utep.edu/dissertations/AAI28261130