Wavelet based detection of scaling behaviour in noisy experimental data

Autor:	Contoyiannis, Yiannis F., Potirakis, Stelios, Diakonos, Fotios K.
Rok vydání:	2019
Předmět:	Physics - Data Analysis Statistics and Probability Nonlinear Sciences - Adaptation and Self-Organizing Systems
Zdroj:	Phys. Rev. E 101, 052104 (2020)
Druh dokumentu:	Working Paper
DOI:	10.1103/PhysRevE.101.052104
Popis:	The detection of power-laws in real data is a demanding task for several reasons. The two, more frequently met, being: (i) real data possess noise which affects significantly the power-law tails and (ii) there is no solid tool for the discrimination between a power-law, valid in a specific range of scales, from other functional forms like log-normal or stretched exponential distributions. In the present report we demonstrate, employing simulated and real data, that using wavelets it is possible to overcome both of the above mentioned difficulties and achieve a secure detection of a power-law and an accurate estimation of the associated exponent. Comment: 16 pages, 4 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1905.01153 Zobrazit plný text záznamu View this record from Arxiv