Time-averaged mean squared displacement ratio test for Gaussian processes with unknown diffusion coefficient
| Autor: | Katarzyna Maraj, Dawid Szarek, Agnieszka Wyłomańska, Grzegorz Sikora |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Fractional Brownian motion
Anomalous diffusion Applied Mathematics Ratio test Gaussian General Physics and Astronomy Statistical and Nonlinear Physics Quadratic form (statistics) Mean squared displacement symbols.namesake symbols Statistical physics Diffusion (business) Gaussian process Mathematical Physics Mathematics |
| Zdroj: | Chaos: An Interdisciplinary Journal of Nonlinear Science. 31:073120 |
| ISSN: | 1089-7682 1054-1500 |
| DOI: | 10.1063/5.0054119 |
| Popis: | The time-averaged mean squared displacement (TAMSD) is one of the most common statistics used for the analysis of anomalous diffusion processes. Anomalous diffusion is manifested by non-linear (mostly power-law) characteristics of the process in contrast to normal diffusion where linear characteristics are expected. One can distinguish between sub- and super-diffusive processes. We consider Gaussian anomalous diffusion models and propose a new approach used for their testing. This approach is based on the TAMSD ratio statistic for different time lags. Similar to the TAMSD, this statistic exhibits a specific behavior in the anomalous diffusion regime. Through its structure, it is independent of the diffusion coefficient, which, in general, does not influence anomalous diffusion behavior. Thus, the TAMSD ratio-based approach does not require preliminary knowledge of the diffusion coefficient's value, in contrast to the TAMSD-approach, where this value is crucial in the testing procedure. Based on the quadratic form representation of the TAMSD ratio, we calculate its main characteristics and propose a step-by-step testing procedure that can be applied for any Gaussian process. For the anomalous diffusion model used here, namely, the fractional Brownian motion, we demonstrate the effectiveness of the proposed methodology. We show that the new approach outperforms the TAMSD-based one, especially for small sample sizes. Finally, the methodology is applied to the real data from the financial market. |
| Databáze: | OpenAIRE |
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