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VOL. 45 | 2004 Self-similar processes, fractional Brownian motion and statistical inference

Abstract

Self-similar stochastic processes are used for stochastic modeling whenever it is expected that long range dependence may be present in the phenomenon under consideration. After discussing some basic concepts of self-similar processes and fractional Brownian motion, we review some recent work on parametric and nonparametric inference for estimation of parameters for linear systems of stochastic differential equations driven by a fractional Brownian motion.

Information

Published: 1 January 2004
First available in Project Euclid: 28 November 2007

zbMATH: 1268.62101
MathSciNet: MR2126890

Digital Object Identifier: 10.1214/lnms/1196285383

Subjects:
Primary: 62M09
Secondary: 60G15

Rights: Copyright © 2004, Institute of Mathematical Statistics

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Vol. 45 • 1 January 2004
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