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• W2765853845 abstract "Abstract In this paper, we revisit maximum likelihood methods for identification of errors-in-variables systems. We assume that the system admits a parametric description, and that the input is a stochastic ARMA process. The cost function associated with the maximum likelihood criterion is minimized by introducing a new iterative solution scheme based on the expectation-maximization method, which proves fast and easily implementable. Numerical simulations show the effectiveness of the proposed method." @default.
• W2765853845 created "2017-11-10" @default.
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• W2765853845 date "2017-07-01" @default.
• W2765853845 modified "2023-10-02" @default.
• W2765853845 title "On maximum likelihood identification of errors-in-variables models" @default.
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• W2765853845 doi "https://doi.org/10.1016/j.ifacol.2017.08.634" @default.
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