Spatially Adaptive Estimation of Spectrum
Abstract
A time series may be analyzed either in the time or in the frequency domain. When working in the frequency domain, the main objective is to estimate the underlying spectrum. Various approaches have been proposed to this end, but most are based on smoothing the periodogram using a single smoothing parameter across all Fourier frequencies. Such a global smoothing parameter may result in a biased estimate. To improve the estimation, in this paper, we smooth the log periodogram by placing a dynamic shrinkage prior, such that varying degrees of smoothing may be applied to different regions of the Fourier frequencies, resulting in a less biased estimate of the spectrum.
Subject Area
Statistics|Theoretical Mathematics
Recommended Citation
Xie, Yi, "Spatially Adaptive Estimation of Spectrum" (2023). ETD Collection for University of Texas, El Paso. AAI30527366.
https://scholarworks.utep.edu/dissertations/AAI30527366