Synthetic detection of change point and outliers in bilinear time series models

DOI10.1080/00207721.2013.777983zbMath1316.93107OpenAlexW2000361753MaRDI QIDQ5265599

Ping Chen, Linyuan Li, Jing Yang

Publication date: 28 July 2015

Published in: International Journal of Systems Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207721.2013.777983

zbMATH Keywords

outlierstime seriesGibbs samplerdynamic systemschange pointbilinear model

Mathematics Subject Classification ID

Estimation and detection in stochastic control theory (93E10) Identification in stochastic control theory (93E12) Time series analysis of dynamical systems (37M10)

Related Items (1)

Robust maximum likelihood estimation for stochastic state space model with observation outliers

Cites Work

• On the detection of changes in autoregressive time series. I: Asymptotics.
• Detection of additive outliers in bilinear time series
• Detection of multiple change-points in multivariate time series
• Identification and verification of chaotic dynamics in a missile system from experimental time series
• Gaussian process internal model control
• Delay-dependent stability analysis for discrete-time singular Markovian jump systems with time-varying delay
• Distributed modal identification using restricted auto regressive models
• BAYESIAN ANALYSIS OF AUTOREGRESSIVE TIME SERIES VIA THE GIBBS SAMPLER
• Influence Diagnostics and Outlier Tests for Semiparametric Mixed Models
• Separable lower triangular bilinear model
• Estimating a change point in the long memory parameter
• Outlier Detection And Estimation In NonLinear Time Series
• Parameter Estimation and Subset Selection for Separable lower Triangular Bilinear Models

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