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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Foundation for Open Access Statistics
2023-01-01
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| Series: | Journal of Statistical Software |
| Subjects: | |
| Online Access: | https://www.jstatsoft.org/index.php/jss/article/view/4229 |
| _version_ | 1797813956054614016 |
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| author | Leonardo Rydin Gorjão Dirk Witthaut Pedro G. Lind |
| author_facet | Leonardo Rydin Gorjão Dirk Witthaut Pedro G. Lind |
| author_sort | Leonardo Rydin Gorjão |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T08:00:22Z |
| format | Article |
| id | doaj.art-b0b5ecfc377d46c6b0881d817381ccfe |
| institution | Directory Open Access Journal |
| issn | 1548-7660 |
| language | English |
| last_indexed | 2024-03-13T08:00:22Z |
| publishDate | 2023-01-01 |
| publisher | Foundation for Open Access Statistics |
| record_format | Article |
| series | Journal of Statistical Software |
| spelling | doaj.art-b0b5ecfc377d46c6b0881d817381ccfe2023-06-01T18:48:03ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602023-01-0110512210.18637/jss.v105.i044007jumpdiff: A Python Library for Statistical Inference of Jump-Diffusion Processes in Observational or Experimental Data SetsLeonardo Rydin Gorjão0https://orcid.org/0000-0001-5513-0580Dirk Witthaut1https://orcid.org/0000-0002-3623-5341Pedro G. Lind2https://orcid.org/0000-0002-8176-666XNorwegian University of Life Sciences (NMBU)Forschungszentrum JülichOsloMetWe 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.https://www.jstatsoft.org/index.php/jss/article/view/4229stochastic differential equationsjump-diffusion processeskramers–moyal expansionkramers–moyal coefficientspython |
| spellingShingle | Leonardo Rydin Gorjão Dirk Witthaut Pedro G. Lind jumpdiff: A Python Library for Statistical Inference of Jump-Diffusion Processes in Observational or Experimental Data Sets Journal of Statistical Software stochastic differential equations jump-diffusion processes kramers–moyal expansion kramers–moyal coefficients python |
| title | jumpdiff: A Python Library for Statistical Inference of Jump-Diffusion Processes in Observational or Experimental Data Sets |
| title_full | jumpdiff: A Python Library for Statistical Inference of Jump-Diffusion Processes in Observational or Experimental Data Sets |
| title_fullStr | jumpdiff: A Python Library for Statistical Inference of Jump-Diffusion Processes in Observational or Experimental Data Sets |
| title_full_unstemmed | jumpdiff: A Python Library for Statistical Inference of Jump-Diffusion Processes in Observational or Experimental Data Sets |
| title_short | jumpdiff: A Python Library for Statistical Inference of Jump-Diffusion Processes in Observational or Experimental Data Sets |
| title_sort | jumpdiff a python library for statistical inference of jump diffusion processes in observational or experimental data sets |
| topic | stochastic differential equations jump-diffusion processes kramers–moyal expansion kramers–moyal coefficients python |
| url | https://www.jstatsoft.org/index.php/jss/article/view/4229 |
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