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- W2066749907 abstract "Develops two sequential or on-line estimation schemes in the time domain for dynamic shock-error models which are special cases of errors-in-variables models. The author's approach utilizes a state-space representation of the model, Kalman filtering techniques, and on-line algorithms. The first on-line algorithm is based on the expectation-maximization algorithm and uses a recursive Gauss-Newton scheme to maximize the Kullback Leibler information measure. The second on-line algorithm the author proposes is a gradient-based scheme and uses stochastic approximations to maximize the log likelihood. In comparison to the off-line maximum likelihood estimation scheme used in Ghosh (1989), the author's on-line algorithms have significantly reduced computational costs and negligible memory requirements. Simulations illustrate the satisfactory performance of the algorithms in estimating errors-in-variables systems with parameters that vary slowly with time or undergo infrequent jump changes.< <ETX xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>></ETX>" @default.
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- W2066749907 date "1994-05-01" @default.
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- W2066749907 title "On-line estimation of dynamic shock-error models based on the Kullback Leibler information measure" @default.
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- W2066749907 doi "https://doi.org/10.1109/9.284909" @default.
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