A Direct Method for the Langevin-Analysis of Multidimensional Stochastic Processes with Strong Correlated Measurement Noise
DOI10.1007/978-3-319-28725-6_1zbMath1366.62178OpenAlexW2508150057MaRDI QIDQ5280119
Teresa Scholz, Vitor V. Lopes, Matthias Wächter, Frank Raischel, Bernd Lehle, Pedro G. Lind
Publication date: 20 July 2017
Published in: Time Series Analysis and Forecasting (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/978-3-319-28725-6_1
Estimation in multivariate analysis (62H12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) General second-order stochastic processes (60G12) Markov processes: estimation; hidden Markov models (62M05) Applications of Brownian motions and diffusion theory (population genetics, absorption problems, etc.) (60J70)
Uses Software
Cites Work
- Stochastic time series with strong, correlated measurement noise: Markov analysis in \(N\) dimensions
- Kernel-based regression of drift and diffusion coefficients of stochastic processes
- The Fokker-Planck equation. Methods of solutions and applications.
- Parameter-free resolution of the superposition of stochastic signals
- On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming
- Experimental indications for Markov properties of small-scale turbulence
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