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Diagnostics subspace identification method of linear state-space model with observation outliers

Diagnostics subspace identification method of linear state-space model with observation outliers

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The authors propose a diagnostic technique for the state-space model fitting of time series by deleting some observations and measuring the change in the parameter estimates. They consider this approach in order to distinguish an observational outlier from an innovational one. Thus, they present a robust subspace identification algorithm that is less sensitive to outliers. A Monte Carlo simulation for a vibrating structure model demonstrates the effectiveness of the proposed algorithm and its ability to detect outliers in the measurements as well as the dynamical state.

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