On the generalized Cramer-Rao bound for the estimation of the location

It is shown that the generalized Gaussian distribution maximizes the generalized Cramer-Rao (CR) bound for the pth absolute central moment of any classical location parameter unbiased estimator. The underlying maximization is taken over the class of distributions with fixed and finite pth-order moment and exhibits particular utility in minimax designs as well as in worst-case performance analysis. The relationship between the generalized Gaussian density and the generalized CR bound is further examined for the model of a mixture of generalized Gaussian distributions as web as for scenarios where multiple independent generalized Gaussian observations are involved.