Work

Optimal parameters for anomalous-diffusion-exponent estimation from noisy data

Year: 2018

Type: article

Abstract: The most common way of estimating the anomalous-diffusion exponent from single-particle trajectories consists in a linear fitting of the dependence of the time averaged mean square displacement on the lag time at the log-log scale. However, various measurement noises that are unavoidably present in experimental data can strongly deteriorate the quality of this estimation procedure and bias the estimated exponent. To investigate the impact of noises and to improve the estimation quality, we compare three approaches for estimating the anomalous-diffusion exponent and check their efficiency on fractional Brownian motion corrupted by Gaussian noise. We discuss how the parameters of both the anomalous-diffusion model and the estimation techniques influence the estimated exponent. We show that the conventional linear fitting is the least optimal method for the analysis of noisy data.

Source: Physical review. E

DOI: 10.1103/physreve.98.062139

Authors: Yann Lanoiselée, Grzegorz Sikora, Aleksandra Grzesiek, Denis S. Grebenkov, Agnieszka Wyłomańska

Institutions: Centre National de la Recherche Scientifique, École Polytechnique, Laboratoire de Physique de la Matière Condensée, Wrocław University of Science and Technology

Language: English

Topic: Fractional Differential Equations Solutions

Subfield: Modeling and Simulation

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: Agence Nationale de la Recherche, Narodowe Centrum Nauki

Awards: INferring Anomalous Diffusions in LIving Cells from single particle tracking experiments, G5571689205