Robust Estimation and Model Order Selection for Signal Processing (2014)
Abstract / truncated to 115 words
In this thesis, advanced robust estimation methodologies for signal processing are developed and analyzed. The developed methodologies solve problems concerning multi-sensor data, robust model selection as well as robustness for dependent data. The work has been applied to solve practical signal processing problems in different areas of biomedical and array signal processing. In particular, for univariate independent data, a robust criterion is presented to select the model order with an application to corneal-height data modeling. The proposed criterion overcomes some limitations of existing robust criteria. For real-world data, it selects the radial model order of the Zernike polynomial of the corneal topography map in accordance with clinical expectations, even if the measurement conditions for the ... toggle 19 keywordsrobustness – dependent data – multi-sensor data – influence function – breakdown point – maximum bias curve – estimation – signal processing – robust estimation – robust model order selection – autoregressive moving-average (arma) – videokeratoscopy – artifacts – corneal-height data – forecasting – spatial time-frequency distribution – bounded influence propagation (bip) τ-estimator – electrocardiogram – intracranial pressure
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