  Assume that several competing methods are available to estimate a parameter
in a given statistical model. The aim of estimator averaging is to provide a
new estimator, built as a linear combination of the initial estimators, that
achieves better properties, under the quadratic loss, than each individual
initial estimator. This contribution provides an accessible and clear overview
of the method, and investigates its performances on standard spatial point
process models. It is demonstrated that the average estimator clearly improves
on standard procedures for the considered models. For each example, the code to
implement the method with the R software (which only consists of few lines) is
provided.
