On characterizations of the covariance matrix

Abstract: The covariance matrix is well-known for its following properties: affine equivariance, additivity, independence property and full affine equivariance. Generalizing the first one leads into the study of scatter functionals, commonly used as plug-in estimators to replace the covariance matrix in robust statistics. However, if the application requires also some of the other properties of the covariance matrix listed earlier, the success of the plug-in depends on whether the candidate scatter functional possesses these. In this short note we show that under natural regularity conditions the covariance matrix is the only scatter functional that is additive and the only scatter functional that is full affine equivariant.