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Linear regression quantifies the linear relationship between paired sets of input and output observations. The well known least-squares regression optimizes the performance criterion defined by the residual error, but is highly sensitive to uncertainties or perturbations in the observations. Robust least-squares algorithms have been developed to optimize(More)
Linear regression with high uncertainties in the measurements, model structure and model permanence is a major challenging problem. Standard regression techniques are based on optimizing a certain performance criterion, usually the mean squared error, and are highly sensitive to uncertainties. Regularization methods have been developed to address the(More)
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