On a Minimax Estimate for the Mean of a Normal Random Vector Under a Generalized Quadratic Loss Function

Abstract

An admissible minimax estimate for the mean of a normal random vector with known covariance is derived for a generalized quadratic loss function. This loss function is quadratic in both the estimation error and the unknown mean. The estimate is derived using the method of least favorable prior distributions. The decision rule is linear, and the least favorable prior distribution for the unknown mean is normal with zero mean. The covariance of this least favorable normal distribution is determined by the solution of a certain nonlinear algebraic matrix equation.