Simultaneous equivariant estimation of the parameters of linear models

Consider a family of distributions which is invariant under a group of transformations. In this paper, we define an optimality criterion with respect to an arbitrary convex loss function and we prove a characterization theorem for an equivariant estimator to be optimal. Then we consider a linear model Y = X beta + epsilon, in which epsilon has a multivariate distribution with mean vector zero and has a density belonging to a scale family with scale parameter sigma. Also we assume that the underlying Family of distributions is invariant with respect to a certain group of transformations. First, we find the class of all equivariant estimators of regression parameters and the powers of sigma. By using the characterization theorem we discuss the simultaneous equivariant estimation of the parameters of the linear model.