- -

Improving Kernel Methods for Density Estimation in Random Differential Equations Problems

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

Improving Kernel Methods for Density Estimation in Random Differential Equations Problems

Show full item record

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

Files in this item

Item Metadata

Title: Improving Kernel Methods for Density Estimation in Random Differential Equations Problems
Author: Cortés, J.-C. Jornet Sanz, Marc
UPV Unit: Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada
Issued date:
Abstract:
[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 ...[+]
Subjects: Probability density estimation , Monte Carlo simulation , Parametric method , Random differential equation
Copyrigths: Reconocimiento (by)
Source:
Mathematical and Computational Applications (Online). (eissn: 2297-8747 )
DOI: 10.3390/mca25020033
Publisher:
MDPI AG
Publisher version: https://doi.org/10.3390/mca25020033
Project ID:
AEI/MTM2017-89664-P-AR
Thanks:
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.[+]
Type: Artículo

This item appears in the following Collection(s)

Show full item record