Bayesian nonparametric spectral analysis of locally stationary processes*

About this item

Title

Bayesian nonparametric spectral analysis of locally stationary processes*

Content partner

The University of Auckland Library

Collection

ResearchSpace@Auckland

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-...

Read more

Format

Research paper

Research format

Journal article

Date created

2025-12-10

Creator

Tang, Yifu / Kirch, Claudia / Lee, Jeong Eun / Meyer, Renate

URL

https://hdl.handle.net/2292/74378

Related subjects

0104 Statistics / 1403 Econometrics / 1603 Demography / 3802 Econometrics / 4905 Statistics

What is the copyright status of this item?

All Rights Reserved

This item is all rights reserved, which means you'll have to get permission from The University of Auckland Library before using it.

More Information

The University of Auckland Library has this to say about the rights status of this item:

Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. Copyright: The authors

You can learn more about the rights status of this item at:

• https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm
• https://authorservices.taylorandfrancis.com/research-impact/sharing-versions-of-journal-articles/

What can I do with this item?

You must always check with The University of Auckland Library to confirm the specific terms of use, but this is our understanding:

Non-infringing use

NZ Copyright law does not prevent every use of a copyright work. You should consider what you can and cannot do with a copyright work.

No sharing

You may not copy and/or share this item with others without further permission. This includes posting it on your blog, using it in a presentation, or any other public use.

No modifying

You are not allowed to adapt or remix this item into any other works.

No commercial use

You may not use this item commercially.