LEAVE-K -OUT DIAGNOSTICS IN STATE-SPACE MODELS

DOI10.1111/1467-9892.00304zbMath1115.62104OpenAlexW2079295593MaRDI QIDQ3440786

Tommaso Proietti

Publication date: 29 May 2007

Published in: Journal of Time Series Analysis (Search for Journal in Brave)

Full work available at URL: http://edoc.hu-berlin.de/18452/4047

zbMATH Keywords

outlierssmoothingKalman filternonstationary state-space model

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Linear inference, regression (62J99) Diagnostics, and linear inference and regression (62J20)

Related Items (5)

An outlier detection scheme for dynamical sequential datasets ⋮ Combining Bayesian method and Kalman smoother for detection additive outlier patches in autoregressive time series ⋮ State space models for time series with patches of unusual observations ⋮ Signal extraction and filtering by linear semiparametric methods ⋮ Assessing influence in Gaussian long-memory models

Cites Work

• Detecting shocks: Outliers and breaks in time series
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