Abstract
This chapter deals with the problem of constructing a state-space model for a stochastic process from a finite number of estimated covariance lags. The approach is to first obtain a high-order model which exactly matches the estimated covariance sequence, and then use balanced model reduction techniques to obtain a lower order model which approximates the given sequence. It is shown that balanced models can be obtained from a realization algorithm which uses an infinite covariance sequence. Scaling ideas are then introduced so that balanced realizations can be obtained from finite covariance sequences.
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© 1986 Kluwer Academic Publishers, Boston
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Vaccaro, R.J. (1986). Finite-Data Algorithms for Approximate Stochastic Realization. In: Desai, U.B. (eds) Modelling and Application of Stochastic Processes. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-2267-2_5
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DOI: https://doi.org/10.1007/978-1-4613-2267-2_5
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