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001005789 1001_ $$0P:(DE-HGF)0$$aGorjão, Leonardo Rydin$$b0$$eCorresponding author
001005789 245__ $$ajumpdiff : A Python library for statistical inference of jump-diffusion processes in observational or experimental data sets
001005789 260__ $$aLos Angeles, Calif.$$bUCLA, Dept. of Statistics$$c2023
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001005789 520__ $$aWe introduce a Python library, called jumpdiff, which includes all necessary functions to assess jump-diffusion processes. This library includes functions which compute a set of non-parametric estimators of all contributions composing a jump-diffusion process, namely the drift, the diffusion, and the stochastic jump strengths. Having a set of measurements from a jump-diffusion process, jumpdiff is able to retrieve the evolution equation producing data series statistically equivalent to the series of measurements. The back-end calculations are based on second-order corrections of the conditional moments expressed from the series of Kramers-Moyal coefficients. Additionally, the library is also able to test if stochastic jump contributions are present in the dynamics underlying a set of measurements. Finally, we introduce a simple iterative method for deriving secondorder corrections of any Kramers-Moyal coefficient.
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001005789 7001_ $$0P:(DE-Juel1)162277$$aWitthaut, Dirk$$b1$$ufzj
001005789 7001_ $$0P:(DE-HGF)0$$aLind, Pedro G.$$b2
001005789 773__ $$0PERI:(DE-600)2010240-9$$a10.18637/jss.v105.i04$$gVol. 105, no. 4$$n4$$p1$$tJournal of statistical software$$v105$$x1548-7660$$y2023
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