title: Improved estimators for fractional Brownian motion via the expectation-maximization algorithm.
abstract: Fractional Brownian motion (FBM) provides a useful model for many physical and biological phenomena demonstrating long-term dependencies and 1/f-type spectral behavior. In this model, only one parameter is necessary to describe the complexity of the data, the Hurst exponent (H). The development of accurate estimators for H is a topic of interest in areas such as radiographic image processing and heart rate variability (HRV) analysis. The development of a 1D estimator utilizing the Expectation-Maximization (EM) algorithm is explained; the estimator is designed for a signal model consisting of FBM and additive white noise. The performance of this estimator is tested on simulated noisy FBM data sets, and found to provide more accurate estimates of H than a maximum likelihood estimator for FBM and the detrended fluctuation analysis (DFA) method.
authors.affiliation: Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA.
authors.forename: Russell
authors.initials: R
authors.lastname: Fischer
authors.forename: Metin
authors.initials: M
authors.lastname: Akay
mesh_terms: D000465:Algorithms; D055592:Biophysical Phenomena; D001703:Biophysics; D006339:Heart Rate; D016013:Likelihood Functions; D016011:Normal Distribution