About this item
- Title
- Bayesian Nonparametric Spectral Analysis of Locally Stationary Processes
- Content partner
- University of Otago
- Collection
- Otago University Research Archive
- Description
Stationarity plays a pivotal role in time series analysis. It is not only the basis for the derivation of general asymptotic theory but it also allows an efficient analysis in the frequency domain via the Whittle likelihood, based on the asymptotic independence of the Fourier coefficients. However, many regularly sampled data derived from the observation of physical or ecological processes, for instance, are only locally stationary. They exhibit slowly evolving spectra and asymptotically non-...
- Format
- Research paper
- Research format
- Scholarly text / Journal article
- Date created
- 2026-02-17
- Creator
- Tang, Yifu / Kirch, Claudia / Lee, Jeong Eun / Meyer, Renate
- URL
- https://hdl.handle.net/10523/49900
- Related subjects
- Bernstein-Dirichlet process prior / Locally stationary time series / Model selection / Moving periodogram / Time-varying spectral density
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Copyright © The Author(s) 2026. This work was first published in Journal of the American Statistical Association (Taylor & Francis). This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly attributed to the creator(s) and the source, is not altered, transformed, or built upon in any way, and a link to the Creative Commons license is provided. / CC BY-NC-ND V4.0
You can learn more about the rights status of this item at: http://creativecommons.org/licenses/by-nc-nd/4.0
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