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  • Article

    Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in Technometrics on 27/03/2014, available online: http://wwww.tandfonline.com/10.1080/00401706.2014.902776

    Accepted author manuscript, 360 KB, PDF-document

    Available under license: CC BY: Creative Commons Attribution 4.0 International License

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    239 KB, PDF-document


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The uncertainty of storm season changes: quantifying the uncertainty of autocovariance changepoints

Research output: Contribution to journalJournal article

<mark>Journal publication date</mark>13/07/2015
Issue number2
Number of pages13
Pages (from-to)194-206
Early online date27/03/14
<mark>Original language</mark>English


In oceanography, there is interest in determining storm season changes for logistical reasons such as equipment maintenance scheduling. In particular, there is interest in capturing the uncertainty associated with these changes in terms of the number and location of them. Such changes are associated with autocovariance changes. This paper proposes a framework to quantify the uncertainty of autocovariance changepoints in time series motivated by this oceanographic application. More specifically, the framework considers time series under the Locally Stationary Wavelet framework, deriving a joint density for scale processes in the raw wavelet periodogram. By embedding this density within a Hidden Markov Model framework, we consider changepoint characteristics under this multiscale setting. Such a methodology allows us to model changepoints and their uncertainty for a wide range of models, including piecewise second-order stationary processes, for example piecewise Moving Average processes.

Bibliographic note

This is an Accepted Manuscript of an article published by Taylor //////