Linear minimax regret estimation of deterministic parameters with bounded data uncertainties
Eldar, Y.C.; Ben-Tal, A.; Nemirovski, A.
Signal Processing, IEEE Transactions on
Volume 52, Issue 8, Aug. 2004 Page(s): 2177 - 2188
Digital Object Identifier   10.1109/TSP.2004.831144
Summary: 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.

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