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Classical least squares estimators are well-known to be robust with respect to moment assumptions concerning the error distribution in a wide variety of finite-dimensional statistical problems; generally only a second moment assumption is required for least squares estimators to maintain the same rate of convergence that they would satisfy if the errors were assumed to be Gaussian.
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