Autocovariance Estimation in the Presence of Changepoints

Colin Gallagher; Rebecca Killick; Robert Lund; Xueheng Shi

doi:10.1007/s42952-022-00173-5

Autocovariance Estimation in the Presence of Changepoints

Colin Gallagher, Rebecca Killick, Robert Lund, Xueheng Shi

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Abstract

This article studies estimation of a stationary autocovariance structure in the presence of an unknown number of mean shifts. Here, a Yule–Walker moment estimator for the autoregressive parameters in a dependent time series contaminated by mean shift changepoints is proposed and studied. The estimator is based on first order differences of the series and is proven consistent and asymptotically normal when the number of changepoints m and the series length N satisfy m/ N→ 0 as N→ ∞.

Original language	English
Pages (from-to)	1021-1040
Number of pages	20
Journal	Journal of the Korean Statistical Society
Volume	51
Issue number	4
Early online date	6/06/2022
DOIs	https://doi.org/10.1007/s42952-022-00173-5
Publication status	Published - 31/12/2022

User-defined Keywords

Autoregression
Differencing
Robustness
Rolling Windows
Segmentation
Yule-Walker Estimates

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10.1007/s42952-022-00173-5

Autocovariance_JKSSaccepted
The final publication is available at Springer via http://dx.doi.org/10.1007/s42952-022-00173-5
Accepted author manuscript, 623 KBLicence: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

https://link.springer.com/article/10.1007/s42952-022-00173-5#article-info

DSI: Effective Diagnosis and Treatment of Age-related Disease Through Time-varying Modelling
Kerns, J. (Co-Investigator), Killick, R. (Principal Investigator), Gaffney, C. (Co-Investigator) & Holland, C. (Co-Investigator)
Engineering and Physical Sciences Research Council
1/03/21 → 30/09/24
Project: Research
Detecting soil degradation and restoration through a novel coupled sensor and machine learning framework
Killick, R. (Co-Investigator), Quinton, J. (Principal Investigator), Nemeth, C. (Co-Investigator), Davies, J. (Co-Investigator), James, M. (Co-Investigator) & Gong, M. (Research Associate)
NERC
31/01/20 → 16/09/24
Project: Research
Methodologically Enhanced Virtual Labs for Early Warning of Significant or Catastrophic Change in Ecosystems: Changepoints for a Changing Planet
Blair, G. (Principal Investigator), Killick, R. (Co-Investigator) & Eckley, I. (Co-Investigator)
NERC
15/11/19 → 14/05/21
Project: Research

Cite this

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title = "Autocovariance Estimation in the Presence of Changepoints",

abstract = "This article studies estimation of a stationary autocovariance structure in the presence of an unknown number of mean shifts. Here, a Yule–Walker moment estimator for the autoregressive parameters in a dependent time series contaminated by mean shift changepoints is proposed and studied. The estimator is based on first order differences of the series and is proven consistent and asymptotically normal when the number of changepoints m and the series length N satisfy m/ N→ 0 as N→ ∞.",

keywords = "Autoregression, Differencing, Robustness, Rolling Windows, Segmentation, Yule-Walker Estimates",

author = "Colin Gallagher and Rebecca Killick and Robert Lund and Xueheng Shi",

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AU - Gallagher, Colin

AU - Killick, Rebecca

AU - Lund, Robert

AU - Shi, Xueheng

N1 - The final publication is available at Springer via http://dx.doi.org/10.1007/s42952-022-00173-5

PY - 2022/12/31

Y1 - 2022/12/31

N2 - This article studies estimation of a stationary autocovariance structure in the presence of an unknown number of mean shifts. Here, a Yule–Walker moment estimator for the autoregressive parameters in a dependent time series contaminated by mean shift changepoints is proposed and studied. The estimator is based on first order differences of the series and is proven consistent and asymptotically normal when the number of changepoints m and the series length N satisfy m/ N→ 0 as N→ ∞.

AB - This article studies estimation of a stationary autocovariance structure in the presence of an unknown number of mean shifts. Here, a Yule–Walker moment estimator for the autoregressive parameters in a dependent time series contaminated by mean shift changepoints is proposed and studied. The estimator is based on first order differences of the series and is proven consistent and asymptotically normal when the number of changepoints m and the series length N satisfy m/ N→ 0 as N→ ∞.

KW - Autoregression

KW - Differencing

KW - Robustness

KW - Rolling Windows

KW - Segmentation

KW - Yule-Walker Estimates

U2 - 10.1007/s42952-022-00173-5

DO - 10.1007/s42952-022-00173-5

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EP - 1040

JO - Journal of the Korean Statistical Society

JF - Journal of the Korean Statistical Society

IS - 4

ER -

Autocovariance Estimation in the Presence of Changepoints

Abstract

User-defined Keywords

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Projects

DSI: Effective Diagnosis and Treatment of Age-related Disease Through Time-varying Modelling

Detecting soil degradation and restoration through a novel coupled sensor and machine learning framework

Methodologically Enhanced Virtual Labs for Early Warning of Significant or Catastrophic Change in Ecosystems: Changepoints for a Changing Planet

Cite this