Modeling the Dynamics of the Frequent Users of Electronic Commerce in Spain Using Optimization Techniques for Inverse Problems with Uncertainty

In this paper, we retrieve data about the frequent users of electronic commerce during the period 2011–2016 from the Spanish National Institute of Statistics. These data, coming from surveys, have intrinsic uncertainty that we describe using appropriate random variables. Then, we propose a stochasti...

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Autores:
Burgos, Clara
Cortés, Juan Carlos
Lombana, Ivan
Martínez Rodríguez, David
Villanueva, Rafael J.
Tipo de recurso:
Article of journal
Fecha de publicación:
2018
Institución:
Universidad Cooperativa de Colombia
Repositorio:
Repositorio UCC
Idioma:
OAI Identifier:
oai:repository.ucc.edu.co:20.500.12494/41734
Acceso en línea:
https://doi.org/10.15446/dyna.v85n207.68545
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050270297&doi=10.4067%2fS0718-09502018000100079&partnerID=40&md5=90916fcfb18206ca1ad29fa07936e98d
https://hdl.handle.net/20.500.12494/41734
Palabra clave:
Inverse problem
Nonlinear stochastic model
Probability density function
Random optimization computational methods
Uncertainty quantification
Rights
closedAccess
License
http://purl.org/coar/access_right/c_14cb
Description
Summary:In this paper, we retrieve data about the frequent users of electronic commerce during the period 2011–2016 from the Spanish National Institute of Statistics. These data, coming from surveys, have intrinsic uncertainty that we describe using appropriate random variables. Then, we propose a stochastic model to study the dynamics of frequent users of electronic commerce. The goal of this paper is to solve the inverse problem that consists of determining the model parameters as suitable parametric random variables, in such a way the model output be capable of capturing the data uncertainty, at the time instants where sample data are available, via adequate probability density functions. To achieve the aforementioned goal, we propose a computational procedure that involves building a nonlinear objective function, based on statistical moment measures, to be minimized using a variation of the particle swarm optimization algorithm. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.