Date of Award
2018-01-01
Degree Name
Master of Science
Department
Statistics
Advisor(s)
Ori Rosen
Abstract
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.
Language
en
Provenance
Received from ProQuest
Copyright Date
2018-08
File Size
72 pages
File Format
application/pdf
Rights Holder
Yi Xie
Recommended Citation
Xie, Yi, "A Bayesian Model For Spectral Density Estimation" (2018). Open Access Theses & Dissertations. 1561.
https://scholarworks.utep.edu/open_etd/1561