Quantifying model uncertainty for the observed non-Gaussian data by the Hellinger distance-Reference-Cited by-同舟云学术

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Quantifying model uncertainty for the observed non-Gaussian data by the Hellinger distance

Published:2021-05 Issue: Volume:96 Page:105720
ISSN:1007-5704
Container-title:Communications in Nonlinear Science and Numerical Simulation
language:en
Short-container-title:Communications in Nonlinear Science and Numerical Simulation

Author:

Zheng Yayun^ORCID,Yang Fang,Duan Jinqiao^ORCID,Kurths Jürgen

Funder

Jiangsu University

National Natural Science Foundation of China

Ministry of Education and Science

Publisher

Elsevier BV

Subject

Applied Mathematics,Modelling and Simulation,Numerical Analysis

Reference41 articles.

1. Parameter estimation in stochastic differential equations;Bishwal,2008

2. Simulation and inference for stochastic differential equations: with R examples;Iacus,2008

3. Kernel density estimation via diffusion;Botev;Ann Stat,2010

4. Nonparametric estimation of diffusions: a differential equations approach;Papaspiliopoulos;Biometrika,2012

5. Approximate Bayesian computation in population genetics;Beaumont;Genetics,2002

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1. Nonlinear dynamics of discontinuous uncertain oscillators with unilateral constraints;Chaos: An Interdisciplinary Journal of Nonlinear Science;2022-12

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