This title appears in the Scientific Report :
2023
Please use the identifier:
http://dx.doi.org/10.18637/jss.v105.i04 in citations.
Please use the identifier: http://hdl.handle.net/2128/34257 in citations.
jumpdiff : A Python library for statistical inference of jump-diffusion processes in observational or experimental data sets
jumpdiff : A Python library for statistical inference of jump-diffusion processes in observational or experimental data sets
We introduce a Python library, called jumpdiff, which includes all necessary functions to assess jump-diffusion processes. This library includes functions which compute a set of non-parametric estimators of all contributions composing a jump-diffusion process, namely the drift, the diffusion, and th...
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Personal Name(s): | Gorjão, Leonardo Rydin (Corresponding author) |
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Witthaut, Dirk / Lind, Pedro G. | |
Contributing Institute: |
Systemforschung und Technologische Entwicklung; IEK-STE |
Published in: | Journal of statistical software, 105 (2023) 4, S. 1 |
Imprint: |
Los Angeles, Calif.
UCLA, Dept. of Statistics
2023
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DOI: |
10.18637/jss.v105.i04 |
Document Type: |
Journal Article |
Research Program: |
Helmholtz UQ: Uncertainty Quantification - from data to reliable knowledge Societally Feasible Transformation Pathways |
Link: |
OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://hdl.handle.net/2128/34257 in citations.
We introduce a Python library, called jumpdiff, which includes all necessary functions to assess jump-diffusion processes. This library includes functions which compute a set of non-parametric estimators of all contributions composing a jump-diffusion process, namely the drift, the diffusion, and the stochastic jump strengths. Having a set of measurements from a jump-diffusion process, jumpdiff is able to retrieve the evolution equation producing data series statistically equivalent to the series of measurements. The back-end calculations are based on second-order corrections of the conditional moments expressed from the series of Kramers-Moyal coefficients. Additionally, the library is also able to test if stochastic jump contributions are present in the dynamics underlying a set of measurements. Finally, we introduce a simple iterative method for deriving secondorder corrections of any Kramers-Moyal coefficient. |