dc.contributor.author | Rydin Gorjão, Leonardo | |
dc.contributor.author | Witthaut, Dirk | |
dc.contributor.author | Lind, Pedro | |
dc.date.accessioned | 2023-03-22T09:45:01Z | |
dc.date.available | 2023-03-22T09:45:01Z | |
dc.date.created | 2023-01-19T11:24:25Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | Journal of Statistical Software. 2023, 105 (1), 1-22. | |
dc.identifier.issn | 1548-7660 | |
dc.identifier.uri | https://hdl.handle.net/11250/3059733 | |
dc.description.abstract | 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. | |
dc.description.abstract | jumpdiff: A Python Library for Statistical Inference of Jump-Diffusion Processes in Observational or Experimental Data Sets | |
dc.language.iso | eng | |
dc.subject | Parameterestimering | |
dc.subject | Parameter estimation | |
dc.subject | Stokastiske prosesser | |
dc.subject | Stochastic processes | |
dc.title | jumpdiff: A Python Library for Statistical Inference of Jump-Diffusion Processes in Observational or Experimental Data Sets | |
dc.title.alternative | jumpdiff: A Python Library for Statistical Inference of Jump-Diffusion Processes in Observational or Experimental Data Sets | |
dc.type | Peer reviewed | |
dc.type | Journal article | |
dc.description.version | publishedVersion | |
dc.subject.nsi | VDP::Fysikk: 430 | |
dc.subject.nsi | VDP::Physics: 430 | |
dc.source.pagenumber | 1-22 | |
dc.source.volume | 105 | |
dc.source.journal | Journal of Statistical Software | |
dc.source.issue | 1 | |
dc.identifier.doi | 10.18637/jss.v105.i04 | |
dc.identifier.cristin | 2110151 | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 2 | |