Extending the hyper-logistic model to the random setting: new theoretical results with real-world applications
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| dc.contributor.author | Cortés, J.-C.
|
es_ES |
| dc.contributor.author | Navarro-Quiles, Ana
|
es_ES |
| dc.contributor.author | Sferle, Sorina Madalina
|
es_ES |
| dc.date.accessioned | 2025-02-26T19:09:56Z | |
| dc.date.available | 2025-02-26T19:09:56Z | |
| dc.date.issued | 2024-05 | es_ES |
| dc.identifier.issn | 0170-4214 | es_ES |
| dc.identifier.uri | http://hdl.handle.net/10251/214876 | |
| dc.description.abstract | [EN] We develop a full randomization of the classical hyper-logistic growth model by obtaining closed-form expressions for relevant quantities of interest, such as the first probability density function of its solution, the time until a given fixed population is reached, and the population at the inflection point. These results are obtained under very general hypotheses on the distributions of the random model parameters by taking extensive advantage of the so-called random variable transformation method. To illustrate the practical implications of our findings, we apply them to model the growth of multicellular tumor spheroids using empirical data. In this context, we explore two methodologies-the Bayesian approach and the random least mean square method-aimed at effectively addressing the challenge of assigning appropriate distributions to model parameters. This ensures that probabilistic fits accurately capture the inherent uncertainties of tumor growth dynamics. Finally, we notably show that the results obtained using both approaches in the randomized hyper-logistic model align closely with each other, surpassing those yielded by the randomized logistic model. | es_ES |
| dc.description.sponsorship | MCIN/AEI/10.13039/501100011033 (Agencia Estatal de Investigacion),Grant/Award Number:PID2020-115270GB-I00 and PRE2021-101090; FSE+(Fondo SocialEuropeo Plus). | es_ES |
| dc.language | Inglés | es_ES |
| dc.publisher | John Wiley & Sons | es_ES |
| dc.relation.ispartof | Mathematical Methods in the Applied Sciences | es_ES |
| dc.rights | Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) | es_ES |
| dc.subject | Hyper-logistic model | es_ES |
| dc.subject | Random differential equation | es_ES |
| dc.subject | Real-world application | es_ES |
| dc.subject | Uncertainty quantification | es_ES |
| dc.subject.classification | MATEMATICA APLICADA | es_ES |
| dc.title | Extending the hyper-logistic model to the random setting: new theoretical results with real-world applications | es_ES |
| dc.type | Artículo | es_ES |
| dc.identifier.doi | 10.1002/mma.10206 | es_ES |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-115270GB-I00/ES/ECUACIONES DIFERENCIALES ALEATORIAS. CUANTIFICACION DE LA INCERTIDUMBRE Y APLICACIONES/ | es_ES |
| dc.rights.accessRights | Abierto | es_ES |
| dc.contributor.affiliation | Universitat Politècnica de València. Instituto Universitario de Matemática Multidisciplinar - Institut Universitari de Matemàtica Multidisciplinària | es_ES |
| dc.contributor.affiliation | Universitat Politècnica de València. Facultad de Administración y Dirección de Empresas - Facultat d'Administració i Direcció d'Empreses | es_ES |
| dc.description.bibliographicCitation | Cortés, J.; Navarro-Quiles, A.; Sferle, SM. (2024). Extending the hyper-logistic model to the random setting: new theoretical results with real-world applications. Mathematical Methods in the Applied Sciences. https://doi.org/10.1002/mma.10206 | es_ES |
| dc.description.accrualMethod | S | es_ES |
| dc.relation.publisherversion | https://doi.org/10.1002/mma.10206 | es_ES |
| dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
| dc.relation.pasarela | S\517733 | es_ES |
| dc.contributor.funder | European Social Fund | es_ES |
| dc.contributor.funder | AGENCIA ESTATAL DE INVESTIGACION | es_ES |
| dc.contributor.funder | Universitat Politècnica de València | es_ES |
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