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An approach to the nonstationary process analysis

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Summary

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.

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Authors
  1. Yoshiyasu-Hamada Tamura
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The Institute of Statistical Mathematics

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Tamura, YH. An approach to the nonstationary process analysis. Ann Inst Stat Math 39, 227–241 (1987). https://doi.org/10.1007/BF02491462

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  • DOI: https://doi.org/10.1007/BF02491462

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