A regularised estimator for long-range dependent processes

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A regularised estimator for long-range dependent processes. / Vivero, O.; Heath, W.P.
In: Automatica, Vol. 48, No. 2, 01.02.2012, p. 287-296.

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Vivero O, Heath WP. A regularised estimator for long-range dependent processes. Automatica. 2012 Feb 1;48(2):287-296. Epub 2011 Dec 23. doi: 10.1016/j.automatica.2011.07.012

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Vivero, O. ; Heath, W.P. / A regularised estimator for long-range dependent processes. In: Automatica. 2012 ; Vol. 48, No. 2. pp. 287-296.

RIS

TY - JOUR

T1 - A regularised estimator for long-range dependent processes

AU - Vivero, O.

AU - Heath, W.P.

PY - 2012/2/1

Y1 - 2012/2/1

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. In this paper we revisit the maximum likelihood estimator and its computationally efficient approximations: Whittle’s Estimator and the Circulant Embedding Estimator. In particular, this paper proves the asymptotic properties of the Circulant Embedding estimator and establishes the asymptotic equivalence between the three estimators. Furthermore, it is shown that the three methods are ill-conditioned and thus highly sensitive to the presence of measurement errors. Finally, we introduce a regularisation method that improves the performance of the maximum likelihood methods when the datasets have been largely contaminated with errors.

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. In this paper we revisit the maximum likelihood estimator and its computationally efficient approximations: Whittle’s Estimator and the Circulant Embedding Estimator. In particular, this paper proves the asymptotic properties of the Circulant Embedding estimator and establishes the asymptotic equivalence between the three estimators. Furthermore, it is shown that the three methods are ill-conditioned and thus highly sensitive to the presence of measurement errors. Finally, we introduce a regularisation method that improves the performance of the maximum likelihood methods when the datasets have been largely contaminated with errors.

U2 - 10.1016/j.automatica.2011.07.012

DO - 10.1016/j.automatica.2011.07.012

M3 - Erthygl

VL - 48

SP - 287

EP - 296

JO - Automatica

JF - Automatica

IS - 2

ER -