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@ARTICLE{Gorjo:1005789,
      author       = {Gorjão, Leonardo Rydin and Witthaut, Dirk and Lind, Pedro
                      G.},
      title        = {jumpdiff : {A} {P}ython library for statistical inference
                      of jump-diffusion processes in observational or experimental
                      data sets},
      journal      = {Journal of statistical software},
      volume       = {105},
      number       = {4},
      issn         = {1548-7660},
      address      = {Los Angeles, Calif.},
      publisher    = {UCLA, Dept. of Statistics},
      reportid     = {FZJ-2023-01634},
      pages        = {1},
      year         = {2023},
      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.},
      cin          = {IEK-STE},
      ddc          = {510},
      cid          = {I:(DE-Juel1)IEK-STE-20101013},
      pnm          = {1112 - Societally Feasible Transformation Pathways
                      (POF4-111) / HGF-ZT-I-0029 - Helmholtz UQ: Uncertainty
                      Quantification - from data to reliable knowledge
                      (HGF-ZT-I-0029)},
      pid          = {G:(DE-HGF)POF4-1112 / G:(DE-Ds200)HGF-ZT-I-0029},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000923067600001},
      doi          = {10.18637/jss.v105.i04},
      url          = {https://juser.fz-juelich.de/record/1005789},
}