Accessing power-law statistics under experimental constraints

TL;DR

This paper analyzes how experimental constraints like resolution and observation window affect the estimation of power-law statistics in various systems, providing analytical tools and applying them to biological data.

Contribution

It introduces an analytical framework to understand the impact of experimental limitations on power-law data analysis and demonstrates its application to biological transport data.

Findings

Experimental constraints distort observed power-law exponents.

Analytical expressions relate true and observed distributions.

Application to biological data validates the approach.

Abstract

Over the last decades, impressive progresses have been made in many experimental domains, e.g. microscopic techniques such as single-particle tracking, leading to plethoric amounts of data. In a large variety of systems, from natural to socio-economic, the analysis of these experimental data conducted us to conclude about the omnipresence of power-laws. For example, in living systems, we are used to observing anomalous diffusion, e.g. in the motion of proteins within the cell. However, estimating the power-law exponents is challenging. Both technical constraints and experimental limitations affect the statistics of observed data. Here, we investigate in detail the influence of two essential constraints in the experiment, namely, the temporal-spatial resolution and the time-window of the experiment. We study how the observed distribution of an observable is modified by them and analytically derive the expression of the power-law distribution for the observed distribution through the scope of the experiment. We also apply our results on data from an experimental study of the transport of mRNA-protein complexes along dendrites.