  This paper extends the idea of a generalized estimator for a scalar parameter
(Vos, 2022) to multi-dimensional parameters both with and without nuisance
parameters. The title reflects the fact that generalized estimators provide
more than simply another method to find point estimators, and that the methods
to assess generalized estimators differ from those for point estimators. By
generalized estimation we mean the use of generalized estimators together with
an extended definition of information to assess their inferential properties.
We show that Fisher information provides an upper bound for the information
utilized by an estimator and that the score attains this bound.
