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    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

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

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The uncertainty of storm season changes : quantifying the uncertainty of autocovariance changepoints. / Nam, Christopher; Aston, John; Eckley, Idris; Killick, Rebecca.

In: Technometrics, Vol. 57, No. 2, 13.07.2015, p. 194-206.

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Nam, Christopher ; Aston, John ; Eckley, Idris ; Killick, Rebecca. / The uncertainty of storm season changes : quantifying the uncertainty of autocovariance changepoints. In: Technometrics. 2015 ; Vol. 57, No. 2. pp. 194-206.

Bibtex

@article{7d60c3942e4b48628fa621623028fcd3,
title = "The uncertainty of storm season changes: quantifying the uncertainty of autocovariance changepoints",
abstract = "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.",
keywords = "Changepoints, Hidden Markov Models , Locally Stationary Wavelet processes , Oceanography , Sequential Monte Carlo",
author = "Christopher Nam and John Aston and Idris Eckley and Rebecca Killick",
note = "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",
year = "2015",
month = "7",
day = "13",
doi = "10.1080/00401706.2014.902776",
language = "English",
volume = "57",
pages = "194--206",
journal = "Technometrics",
issn = "0040-1706",
publisher = "American Statistical Association",
number = "2",

}

RIS

TY - JOUR

T1 - The uncertainty of storm season changes

T2 - quantifying the uncertainty of autocovariance changepoints

AU - Nam, Christopher

AU - Aston, John

AU - Eckley, Idris

AU - Killick, Rebecca

N1 - 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

PY - 2015/7/13

Y1 - 2015/7/13

N2 - 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.

AB - 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.

KW - Changepoints

KW - Hidden Markov Models

KW - Locally Stationary Wavelet processes

KW - Oceanography

KW - Sequential Monte Carlo

U2 - 10.1080/00401706.2014.902776

DO - 10.1080/00401706.2014.902776

M3 - Journal article

VL - 57

SP - 194

EP - 206

JO - Technometrics

JF - Technometrics

SN - 0040-1706

IS - 2

ER -