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- W2017519265 abstract "Given a finite set of autocorrelations, it is well known that maximization of the entropy functional subject to this data leads to a stable autoregressive model. Since maximization of the entropy functional is equivalent to maximization of the minimum mean square error associated with one-step predictors, the problem of obtaining admissible extensions that maximize the k-step minimum-mean-square prediction error subject to the given autocorrelations has been shown to result in stable autoregressive moving-average (ARMA) extensions. The uniqueness of this true generalization of the maximum-entropy extension is proved here by a constructive procedure in the case of two-step predictors.< <ETX xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>></ETX>" @default.
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- W2017519265 date "1993-01-01" @default.
- W2017519265 modified "2023-10-18" @default.
- W2017519265 title "Uniqueness of a two-step predictor based spectral estimator that generalizes the maximum entropy concept" @default.
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- W2017519265 doi "https://doi.org/10.1109/78.236517" @default.
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