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- W2107467870 abstract "We develop a new linear estimator for estimating an unknown parameter vector x in a linear model in the presence of bounded data uncertainties. The estimator is designed to minimize the worst-case regret over all bounded data vectors, namely, the worst-case difference between the mean-squared error (MSE) attainable using a linear estimator that does not know the true parameters x and the optimal MSE attained using a linear estimator that knows x. We demonstrate through several examples that the minimax regret estimator can significantly increase the performance over the conventional least-squares estimator, as well as several other least-squares alternatives." @default.
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- W2107467870 date "2004-08-01" @default.
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- W2107467870 title "Linear Minimax Regret Estimation of Deterministic Parameters with Bounded Data Uncertainties" @default.
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- W2107467870 doi "https://doi.org/10.1109/tsp.2004.831144" @default.
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