Maximum likelihood estimation of the parameters of fractional Brownian motions

Author

Ninness, Brett

Author_Institution

Centre for Ind. Control Sci., Newcastle Univ., Callaghan, NSW, Australia

Volume

4

fYear

1995

fDate

13-15 Dec 1995

Firstpage

4018

Abstract

This paper provides convergence analysis for maximum likelihood estimation of the parameters describing a particular multiscale process known as fractional Brownian motion. We concentrate on schemes that `pre-whiten´ the available noise-corrupted data via the fast wavelet transform and show that such schemes are strongly consistent and asymptotically efficient. We also analyse the rate of convergence of the maximum likelihood estimates and show that this rate depends on the memory of the fractional process

Keywords

Brownian motion; convergence; maximum likelihood estimation; random processes; wavelet transforms; white noise; asymptotic efficiency; convergence analysis; fast wavelet transform; fractional Brownian motions; fractional process memory; maximum likelihood parameter estimation; multiscale process; noise-corrupted data pre-whitening; strong consistency; 1f noise; Brownian motion; Convergence; Frequency; Maximum likelihood estimation; Motion estimation; Parameter estimation; Semiconductor device noise; Stochastic processes; Wavelet transforms;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1995., Proceedings of the 34th IEEE Conference on

Conference_Location

New Orleans, LA

ISSN

0191-2216

Print_ISBN

0-7803-2685-7

Type

conf

DOI

10.1109/CDC.1995.479234

Filename

479234