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Improving Kernel Methods for Density Estimation in Random Differential Equations Problems

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Improving Kernel Methods for Density Estimation in Random Differential Equations Problems

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Cortés, J.; Jornet Sanz, M. (2020). Improving Kernel Methods for Density Estimation in Random Differential Equations Problems. Mathematical and Computational Applications (Online). 25(2):1-9. https://doi.org/10.3390/mca25020033

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/161986

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Metadatos del ítem

Título: Improving Kernel Methods for Density Estimation in Random Differential Equations Problems
Autor: Cortés, J.-C. Jornet Sanz, Marc
Entidad UPV: Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada
Fecha difusión:
Resumen:
[EN] Kernel density estimation is a non-parametric method to estimate the probability density function of a random quantity from a finite data sample. The estimator consists of a kernel function and a smoothing parameter ...[+]
Palabras clave: Probability density estimation , Monte Carlo simulation , Parametric method , Random differential equation
Derechos de uso: Reconocimiento (by)
Fuente:
Mathematical and Computational Applications (Online). (eissn: 2297-8747 )
DOI: 10.3390/mca25020033
Editorial:
MDPI AG
Versión del editor: https://doi.org/10.3390/mca25020033
Código del Proyecto:
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2017-89664-P/ES/PROBLEMAS DINAMICOS CON INCERTIDUMBRE SIMULABLE: MODELIZACION MATEMATICA, ANALISIS, COMPUTACION Y APLICACIONES/
Agradecimientos:
This work has been supported by the Spanish Ministerio de Economia, Industria y Competitividad (MINECO), the Agencia Estatal de Investigacion (AEI) and Fondo Europeo de Desarrollo Regional (FEDER UE) grant MTM2017-89664-P.[+]
Tipo: Artículo

References

Calatayud, J., Cortés, J.-C., Díaz, J. A., & Jornet, M. (2020). Constructing reliable approximations of the probability density function to the random heat PDE via a finite difference scheme. Applied Numerical Mathematics, 151, 413-424. doi:10.1016/j.apnum.2020.01.012

Calatayud, J., Cortés, J.-C., & Jornet, M. (2018). The damped pendulum random differential equation: A comprehensive stochastic analysis via the computation of the probability density function. Physica A: Statistical Mechanics and its Applications, 512, 261-279. doi:10.1016/j.physa.2018.08.024

Calatayud, J., Cortés, J.-C., Díaz, J. A., & Jornet, M. (2019). Density function of random differential equations via finite difference schemes: a theoretical analysis of a random diffusion-reaction Poisson-type problem. Stochastics, 92(4), 627-641. doi:10.1080/17442508.2019.1645849 [+]
Calatayud, J., Cortés, J.-C., Díaz, J. A., & Jornet, M. (2020). Constructing reliable approximations of the probability density function to the random heat PDE via a finite difference scheme. Applied Numerical Mathematics, 151, 413-424. doi:10.1016/j.apnum.2020.01.012

Calatayud, J., Cortés, J.-C., & Jornet, M. (2018). The damped pendulum random differential equation: A comprehensive stochastic analysis via the computation of the probability density function. Physica A: Statistical Mechanics and its Applications, 512, 261-279. doi:10.1016/j.physa.2018.08.024

Calatayud, J., Cortés, J.-C., Díaz, J. A., & Jornet, M. (2019). Density function of random differential equations via finite difference schemes: a theoretical analysis of a random diffusion-reaction Poisson-type problem. Stochastics, 92(4), 627-641. doi:10.1080/17442508.2019.1645849

Calatayud, J., Cortés, J.-C., Dorini, F. A., & Jornet, M. (2020). Extending the study on the linear advection equation subject to stochastic velocity field and initial condition. Mathematics and Computers in Simulation, 172, 159-174. doi:10.1016/j.matcom.2019.12.014

Jornet, M., Calatayud, J., Le Maître, O. P., & Cortés, J.-C. (2020). Second order linear differential equations with analytic uncertainties: Stochastic analysis via the computation of the probability density function. Journal of Computational and Applied Mathematics, 374, 112770. doi:10.1016/j.cam.2020.112770

Tang, K., Wan, X., & Liao, Q. (2020). Deep density estimation via invertible block-triangular mapping. Theoretical and Applied Mechanics Letters, 10(3), 143-148. doi:10.1016/j.taml.2020.01.023

Botev, Z., & Ridder, A. (2017). Variance Reduction. Wiley StatsRef: Statistics Reference Online, 1-6. doi:10.1002/9781118445112.stat07975

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