Abstract
The identification of a dynamic system with mixed-autoregressive — moving average models raises a nonlinear estimation problem which is solved by algorithms, usually based on least squares theory. This paper presents a new method based on a decision theoretical approach for the identification problem. When a certain criterion is chosen, game theory gives the conditions for the existence of a minimax decision rule, which is fundamental for robust estimation. Simulation studies are presented, starting from time series previously generated by autoregressive-moving average systems excited by gaussian and non-gaussian noise.
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© 1983 D. Reidel Publishing Company
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Teles, M.L.S. (1983). Minimax Estimation of Arma Systems. In: Bucy, R.S., Moura, J.M.F. (eds) Nonlinear Stochastic Problems. NATO ASI Series, vol 104. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-7142-4_9
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DOI: https://doi.org/10.1007/978-94-009-7142-4_9
Publisher Name: Springer, Dordrecht
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