Regularised Estimators for Fractional Gaussian Noise

被引：3

作者：

Vivero, Oskar ^{[1
]}

Heath, William P. ^{[1
]}

机构：

[1] Univ Manchester, Sch Elect & Elect Engn, Control Syst Ctr, Manchester M60 1QD, Lancs, England

来源：

49TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC) | 2010年

关键词：

MAXIMUM-LIKELIHOOD-ESTIMATION; BROWNIAN-MOTION; PARAMETERS; RANGE;

D O I：

10.1109/CDC.2010.5717764

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：5025 / 5030

页数：6

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