Date of Award
Master of Science
When we analyze a stationary time series, one of the questions we often meet is how to estimate its spectral density. Many approaches have been proposed to this end. In this paper we estimate the spectral density of a stationary time series nonparametrically. We fit a nonparametric regression model to the log periodogram and use third-degree B-spline functions as basis functions. Since the the number of basis functions is relatively large, we place priors such as random-walk and regularized horseshoe on the coefficients of the basis functions to avoid over-fitting and smooth the log periodogram.
Received from ProQuest
Xie, Yi, "A Bayesian Model For Spectral Density Estimation" (2018). Open Access Theses & Dissertations. 1561.