Detection and estimation of structural changes and outliers in unobserved components

DOI10.1007/s001800050030zbMath0938.62097OpenAlexW2072371174MaRDI QIDQ1979107

Regina Kaiser

Publication date: 24 May 2000

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s001800050030

zbMATH Keywords

outliersautoregressionmoving averagechange-point detection

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Cites Work

• Monitoring Renal Transplants: An Application of the Multiprocess Kalman Filter
• Non-Gaussian State-Space Modeling of Nonstationary Time Series
• Maximum Likelihood Fitting of ARMA Models to Time Series with Missing Observations
• INITIALIZING THE KALMAN FILTER FOR NONSTATIONARY TIME SERIES MODELS
• Estimation, Prediction, and Interpolation for Nonstationary Series with the Kalman Filter
• Joint Estimation of Model Parameters and Outlier Effects in Time Series
• The Effects of Seat Belt Legislation on British Road Casualties: A Case Study in Structural Time Series Modelling
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