Abstract
The discrete Kalman filter which enables the treatment of incomplete data and outliers is described. The incomplete, or missing observations are included in such a way as to transform the Kalman filter to the case when observations have changing dimensions. In order to treat outliers, the Kalman filter is made robust using the M-estimation principle. Some special cases are considered including a convergence result for recursive parameter estimation in AR(1) process with innovation outliers and missing observations.
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Research partially supported by CICYT grant number TIC93-0702-C02-02
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Cipra, T., Romera, R. Kalman filter with outliers and missing observations. Test 6, 379–395 (1997). https://doi.org/10.1007/BF02564705
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DOI: https://doi.org/10.1007/BF02564705