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- W2577978871 abstract "In this paper we present a novel algorithm for identifying continuous-time autoregressive moving-average models utilizing irregularly sampled data. The proposed algorithm is based on the expectation–maximization algorithm and obtains maximum-likelihood estimates. The proposed algorithm shows a fast convergence rate, good robustness to initial values, and desirable estimation accuracy. Comparisons are made with other algorithms in the literature via numerical examples." @default.
- W2577978871 created "2017-01-26" @default.
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- W2577978871 date "2017-03-01" @default.
- W2577978871 modified "2023-10-13" @default.
- W2577978871 title "EM-based identification of continuous-time ARMA Models from irregularly sampled data" @default.
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- W2577978871 doi "https://doi.org/10.1016/j.automatica.2016.11.020" @default.
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