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- W2023714235 abstract "In this paper we describe an approach to maximum likelihood estimation of linear single input single output (SISO) models when both input and output data are missing. The criterion minimised in the algorithms is the Euclidean norm of the prediction error vector scaled by a particular function of the covariance matrix of the observed output data. We also provide insight into when simpler and in general sub-optimal schemes are indeed optimal. The algorithm has been prototyped in MATLAB, and we report numerical results that support the theory." @default.
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- W2023714235 date "2014-05-02" @default.
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- W2023714235 title "Maximum likelihood estimation of linear SISO models subject to missing output data and missing input data" @default.
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- W2023714235 doi "https://doi.org/10.1080/00207179.2014.913346" @default.
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