A regularised estimator for long-range dependent processes
Allbwn ymchwil: Cyfraniad at gyfnodolyn › Erthygl › adolygiad gan gymheiriaid
StandardStandard
Yn: Automatica, Cyfrol 48, Rhif 2, 01.02.2012, t. 287-296.
Allbwn ymchwil: Cyfraniad at gyfnodolyn › Erthygl › adolygiad gan gymheiriaid
HarvardHarvard
APA
CBE
MLA
VancouverVancouver
Author
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 -