On hsu's model in regression analysis

The paper examplifies with Hsu’s model a general pattern as how to derive results of variance component estimation from well known results on mean estimation, as far as linear model theory is concerned. This ’ dispersion-mean-correspondence‘provides new and short proofs for various theorems from the literature, concerning unbiased invariant quadratic estimators with minimum BAYES risk or minimum variance. For pure variance component models, unbiased non-negative quadratic estimability is characterized in terms of the design matrices.