Abstract
Abstract This article gives a nonlinear version of the Gauss—Markov theorem. It is shown that in a linear regression model y = Xβ + u, the lower bound for the risk matrix of a nonlinear estimator of β belonging to a certain class is the covariance matrix of the Gauss—Markov estimator, provided the distribution of error term u belongs to the class of elliptically symmetric distributions with second moments.
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