Regularised estimators for fractional Gaussian noise

O. Vivero; W.P. Heath

doi:10.1109/CDC.2010.5717764

Regularised estimators for fractional Gaussian noise

O. Vivero
, W.P. Heath

University of Manchester

Allbwn ymchwil: Cyfraniad at gynhadledd › Papur › adolygiad gan gymheiriaid

Crynodeb

There is significant interest in long-range dependent processes since they occur in a wide range of phenomena across different areas of study. Based on the available models capable of describing long-range dependence, various parameter estimation methods have been developed. This paper revisits the maximum likelihood estimator and its computationally efficient approximations: the Whittle Estimator and the Circulant Embedding estimator. Based on the properties of these, a regularisation method for datasets largely contaminated with errors is introduced.

Iaith wreiddiol	Anadnabyddus
Tudalennau	5025-5030
Nifer y tudalennau	6
Dynodwyr Gwrthrych Digidol (DOIs)	https://doi.org/10.1109/CDC.2010.5717764
Statws	Cyhoeddwyd - 22 Chwef 2011
Cyhoeddwyd yn allanol	Ie
Digwyddiad	49th IEEE Conference on Decision and Control (CDC) - Atlanta, Yr Unol Daleithiau Hyd: 15 Rhag 2010 → 17 Rhag 2010

Cynhadledd

Cynhadledd	49th IEEE Conference on Decision and Control (CDC)
Gwlad/Tiriogaeth	Yr Unol Daleithiau
Dinas	Atlanta
Cyfnod	15/12/10 → 17/12/10

Mynediad at Ddogfen

10.1109/CDC.2010.5717764

Dyfynnu hyn

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title = "Regularised estimators for fractional Gaussian noise",

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AU - Heath, W.P.

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N2 - There is significant interest in long-range dependent processes since they occur in a wide range of phenomena across different areas of study. Based on the available models capable of describing long-range dependence, various parameter estimation methods have been developed. This paper revisits the maximum likelihood estimator and its computationally efficient approximations: the Whittle Estimator and the Circulant Embedding estimator. Based on the properties of these, a regularisation method for datasets largely contaminated with errors is introduced.

AB - There is significant interest in long-range dependent processes since they occur in a wide range of phenomena across different areas of study. Based on the available models capable of describing long-range dependence, various parameter estimation methods have been developed. This paper revisits the maximum likelihood estimator and its computationally efficient approximations: the Whittle Estimator and the Circulant Embedding estimator. Based on the properties of these, a regularisation method for datasets largely contaminated with errors is introduced.

U2 - 10.1109/CDC.2010.5717764

DO - 10.1109/CDC.2010.5717764

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T2 - 49th IEEE Conference on Decision and Control (CDC)

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