Linear regression analysis using the relative squared error Original Research Article

In order to determine estimators and predictors in a generalized linear regression model we apply a suitably defined relative squared error instead of the most frequently used absolute squared error. The general solution of a matrix problem is derived leading to minimax estimators and predictors. Furthermore, we consider an important special case, where an analogon to a well-known relation between estimators and predictors holds and where generalized least squares estimators as well as Kuks–Olman and ridge estimators play a prominent role.