TY  - JOUR
AU  - Gorjão, Leonardo Rydin
AU  - Witthaut, Dirk
AU  - Lind, Pedro G.
TI  - jumpdiff : A Python library for statistical inference of jump-diffusion processes in observational or experimental data sets
JO  - Journal of statistical software
VL  - 105
IS  - 4
SN  - 1548-7660
CY  - Los Angeles, Calif.
PB  - UCLA, Dept. of Statistics
M1  - FZJ-2023-01634
SP  - 1
PY  - 2023
AB  - 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000923067600001
DO  - DOI:10.18637/jss.v105.i04
UR  - https://juser.fz-juelich.de/record/1005789
ER  -