Record-breaking statistics for random walks in the presence of measurement error and noise

TL;DR

This paper studies how measurement error and noise affect the record-setting behavior of random walks, revealing a universal growth rate of records with a variable pre-factor depending on error and noise levels.

Contribution

It provides the first analytical characterization of record statistics in noisy and error-prone random walks across all jump distributions.

Findings

Mean record count grows as n^{1/2} universally.

Pre-factor decreases with measurement error in absence of noise.

Pre-factor increases with noise when measurement is perfect.

Abstract

We address the question of distance record-setting by a random walker in the presence of measurement error, $\delta$, and additive noise, $\gamma$ and show that the mean number of (upper) records up to $n$ steps still grows universally as $< R_n> \sim n^{1/2}$ for large $n$ for all jump distributions, including L\'evy flights, and for all $\delta$ and $\gamma$. In contrast to the universal growth exponent of 1/2, the pace of record setting, measured by the pre-factor of $n^{1/2}$, depends on $\delta$ and $\gamma$. In the absence of noise ($\gamma=0$), the pre-factor $S(\delta)$ is evaluated explicitly for arbitrary jump distributions and it decreases monotonically with increasing $\delta$ whereas, in case of perfect measurement $(\delta=0)$, the corresponding pre-factor $T(\gamma)$ increases with $\gamma$. Our analytical results are supported by extensive numerical simulations and qualitatively similar results are found in two and three dimensions.