Abstract
A Bayesian approach to nonstationary process analysis is proposed. Given a set of data, it is divided into several blocks with the same length, and in each block an autoregressive model is fitted to the data. A constraint on the autoregressive coefficients of the successive blocks is considered. This constraint controls the smoothness of the temporal change of spectrum as shown in Section 2. A smoothness parameter, which is called a hyper parameter in this article, is determined with the aid of the minimum ABIC (Akaike Bayesian Information Criterion) procedure. Numerical examples of our procedure are also given.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Annals of the Institute of Statistical Mathematics
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
