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Regularised estimators for fractional Gaussian noise

Research output: Contribution to conference › Paper › peer-review

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Regularised estimators for fractional Gaussian noise. / Vivero, O.; Heath, W.P.
2011. 5025-5030 Paper presented at 49th IEEE Conference on Decision and Control (CDC), Atlanta, United States.

Research output: Contribution to conference › Paper › peer-review

HarvardHarvard

Vivero, O & Heath, WP 2011, 'Regularised estimators for fractional Gaussian noise', Paper presented at 49th IEEE Conference on Decision and Control (CDC), Atlanta, United States, 15/12/10 - 17/12/10 pp. 5025-5030. https://doi.org/10.1109/CDC.2010.5717764

APA

Vivero, O., & Heath, W. P. (2011). Regularised estimators for fractional Gaussian noise. 5025-5030. Paper presented at 49th IEEE Conference on Decision and Control (CDC), Atlanta, United States. https://doi.org/10.1109/CDC.2010.5717764

CBE

Vivero O, Heath WP. 2011. Regularised estimators for fractional Gaussian noise. Paper presented at 49th IEEE Conference on Decision and Control (CDC), Atlanta, United States. https://doi.org/10.1109/CDC.2010.5717764

MLA

Vivero, O. and W.P. Heath Regularised estimators for fractional Gaussian noise. 49th IEEE Conference on Decision and Control (CDC), 15 Dec 2010, Atlanta, United States, Paper, 2011. 6 p. https://doi.org/10.1109/CDC.2010.5717764

VancouverVancouver

Vivero O, Heath WP. Regularised estimators for fractional Gaussian noise. 2011. Paper presented at 49th IEEE Conference on Decision and Control (CDC), Atlanta, United States. doi: 10.1109/CDC.2010.5717764

Author

Vivero, O. ; Heath, W.P. / Regularised estimators for fractional Gaussian noise. Paper presented at 49th IEEE Conference on Decision and Control (CDC), Atlanta, United States.6 p.

RIS

TY - CONF

T1 - Regularised estimators for fractional Gaussian noise

AU - Vivero, O.

AU - Heath, W.P.

PY - 2011/2/22

Y1 - 2011/2/22

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

M3 - Papur

SP - 5025

EP - 5030

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

Y2 - 15 December 2010 through 17 December 2010

ER -