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The Optimality of Linear Estimation Algorithms in Minimax Identification

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Abstract

The optimality of linear estimates in minimax estimation of a stochastically uncertain vector in a linear observation model by mean-square criterion is studied. In the Gaussian case, a uniformly optimal linear estimate is shown to exist in the class of all unbiased estimates. Moreover, it is minimax in the class of all nonlinear estimates if the nonrandom parameters of the observation model are unbounded. If the a priori information on random parameters are given as constraints on the covariance matrix, linear estimates are shown to be minimax.

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  1. Moscow Aviation Institute (State Technical University), Moscow, Russia

    K. V. Semenikhin

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  1. K. V. Semenikhin
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Semenikhin, K.V. The Optimality of Linear Estimation Algorithms in Minimax Identification. Automation and Remote Control 65, 493–503 (2004). https://doi.org/10.1023/B:AURC.0000019382.18600.73

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  • DOI: https://doi.org/10.1023/B:AURC.0000019382.18600.73

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