Abstract
The aim of this paper is to show the approximation of Euler–Maruyama
Acknowledgements
The author would like to thank the referees and the editor for the very useful comments and suggestions, which have helped to improve the paper a lot. The author is very grateful to Professor Mohsine Benabdallah for suggesting this problem, and for fruitful discussions.
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© 2020 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Why simple quadrature is just as good as Monte Carlo
- Describing the Pearson 𝑅 distribution of aggregate data
- Approximation of Euler–Maruyama for one-dimensional stochastic differential equations involving the maximum process
- A Bayesian inference for the penalized spline joint models of longitudinal and time-to-event data: A prior sensitivity analysis
- A Bayesian procedure for bandwidth selection in circular kernel density estimation
Artikel in diesem Heft
- Frontmatter
- Why simple quadrature is just as good as Monte Carlo
- Describing the Pearson 𝑅 distribution of aggregate data
- Approximation of Euler–Maruyama for one-dimensional stochastic differential equations involving the maximum process
- A Bayesian inference for the penalized spline joint models of longitudinal and time-to-event data: A prior sensitivity analysis
- A Bayesian procedure for bandwidth selection in circular kernel density estimation