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

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

Abstract

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.

Original language	Unknown
Pages	5025-5030
Number of pages	6
DOIs	https://doi.org/10.1109/CDC.2010.5717764
Publication status	Published - 22 Feb 2011
Externally published	Yes
Event	49th IEEE Conference on Decision and Control (CDC) - Atlanta, United States Duration: 15 Dec 2010 → 17 Dec 2010

Conference

Conference	49th IEEE Conference on Decision and Control (CDC)
Country/Territory	United States
City	Atlanta
Period	15/12/10 → 17/12/10

Access to Document

10.1109/CDC.2010.5717764

Cite this

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title = "Regularised estimators for fractional Gaussian noise",

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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.

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