Noise-Driven Return Statistics: Scaling and Truncation in Stochastic Storage Processes

Abstract In countless systems, subjected to variable forcing, a key question arises: how much time will a state variable spend away from a given threshold? When forcing is treated as a stochastic process, this can be addressed with first return time distributions. While many studies suggest exponent...

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Autores principales:	Tomás Aquino, Antoine Aubeneau, Gavan McGrath, Diogo Bolster, Suresh Rao
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/20b40ced79da4c19a0f502838b745a1f
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spelling	oai:doaj.org-article:20b40ced79da4c19a0f502838b745a1f2021-12-02T11:52:25ZNoise-Driven Return Statistics: Scaling and Truncation in Stochastic Storage Processes10.1038/s41598-017-00451-x2045-2322https://doaj.org/article/20b40ced79da4c19a0f502838b745a1f2017-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-00451-xhttps://doaj.org/toc/2045-2322Abstract In countless systems, subjected to variable forcing, a key question arises: how much time will a state variable spend away from a given threshold? When forcing is treated as a stochastic process, this can be addressed with first return time distributions. While many studies suggest exponential, double exponential or power laws as empirical forms, we contend that truncated power laws are natural candidates. To this end, we consider a minimal stochastic mass balance model and identify a parsimonious mechanism for the emergence of truncated power law return times. We derive boundary-independent scaling and truncation properties, which are consistent with numerical simulations, and discuss the implications and applicability of our findings.Tomás AquinoAntoine AubeneauGavan McGrathDiogo BolsterSuresh RaoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-6 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tomás Aquino Antoine Aubeneau Gavan McGrath Diogo Bolster Suresh Rao Noise-Driven Return Statistics: Scaling and Truncation in Stochastic Storage Processes
description	Abstract In countless systems, subjected to variable forcing, a key question arises: how much time will a state variable spend away from a given threshold? When forcing is treated as a stochastic process, this can be addressed with first return time distributions. While many studies suggest exponential, double exponential or power laws as empirical forms, we contend that truncated power laws are natural candidates. To this end, we consider a minimal stochastic mass balance model and identify a parsimonious mechanism for the emergence of truncated power law return times. We derive boundary-independent scaling and truncation properties, which are consistent with numerical simulations, and discuss the implications and applicability of our findings.
format	article
author	Tomás Aquino Antoine Aubeneau Gavan McGrath Diogo Bolster Suresh Rao
author_facet	Tomás Aquino Antoine Aubeneau Gavan McGrath Diogo Bolster Suresh Rao
author_sort	Tomás Aquino
title	Noise-Driven Return Statistics: Scaling and Truncation in Stochastic Storage Processes
title_short	Noise-Driven Return Statistics: Scaling and Truncation in Stochastic Storage Processes
title_full	Noise-Driven Return Statistics: Scaling and Truncation in Stochastic Storage Processes
title_fullStr	Noise-Driven Return Statistics: Scaling and Truncation in Stochastic Storage Processes
title_full_unstemmed	Noise-Driven Return Statistics: Scaling and Truncation in Stochastic Storage Processes
title_sort	noise-driven return statistics: scaling and truncation in stochastic storage processes
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/20b40ced79da4c19a0f502838b745a1f
work_keys_str_mv	AT tomasaquino noisedrivenreturnstatisticsscalingandtruncationinstochasticstorageprocesses AT antoineaubeneau noisedrivenreturnstatisticsscalingandtruncationinstochasticstorageprocesses AT gavanmcgrath noisedrivenreturnstatisticsscalingandtruncationinstochasticstorageprocesses AT diogobolster noisedrivenreturnstatisticsscalingandtruncationinstochasticstorageprocesses AT sureshrao noisedrivenreturnstatisticsscalingandtruncationinstochasticstorageprocesses
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Noise-Driven Return Statistics: Scaling and Truncation in Stochastic Storage Processes

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