The theory of KM2O-Langevin equations and its applications to data analysis (III): Deterministic analysis

DOI10.1017/S002776300000684XzbMath0943.62099MaRDI QIDQ4267440

Toshiyuki Yamane, Yasunori Okabe

Publication date: 12 September 2000

Published in: Nagoya Mathematical Journal (Search for Journal in Brave)

zbMATH Keywords

time serieschaotic dynamicslinear causality

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Stationary stochastic processes (60G10) Signal detection and filtering (aspects of stochastic processes) (60G35) Inference from stochastic processes (62M99)

Related Items (4)

A time series analysis of economical phenomena in Japan's lost decade (1): determinacy property of the velocity of money and equilibrium solution ⋮ On a nonlinear risk analysis for stock market indexes ⋮ Detection of changes in non-linear dynamics for time series based on the theory of \(\mathrm{KM}_2 \mathrm O\)-Langevin equations ⋮ Time series analysis with wavelet coefficients

Cites Work

• The theory of KM\(_2\)O-Langevin equations and its applications to data analysis. I: Stationary analysis
• Bootstrap methods: another look at the jackknife
• Application of the theory of \(\text{KM}_ 2\)O-Langevin equations to the linear prediction problem for the multi-dimensional weakly stationary time series
• Application of the theory of \(KM_ 2 O\)-Langevin equations to the nonlinear prediction problem for the one-dimensional strictly stationary time series
• Langevin equations and causal analysis
• The theory of KM₂O-Langevin equations and applications to data analysis (II): Causal analysis (1)
• Nonlinear time series analysis based upon the Fluctuation-Dissipation theorem

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