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- W2163546547 abstract "Principal components algorithms are presented for the problem of fitting an ARMA model to a given segment of a sample sequence of a discrete-time stochastic process, and the model is used to estimate the process power spectrum. To reduce the effects of finite-wordlength errors, balanced state-pace parameterization of the ARMA model is used instead of the more popular difference equation parameterization. Model identification is formulated as a problem of selecting a partial state to span approximately an apparently large-dimensional information interface between the past and the future of the process. Different criteria are used to measure the quality of the approximation, which leads to principal-components algorithms for the problem that are based on singular value decomposition.< <ETX xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>></ETX>" @default.
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- W2163546547 title "Principal components algorithms for ARMA spectrum estimation" @default.
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