A tutorial on estimator averaging in spatial point process models

  • Frédéric Lavancier
  • Paul Rochet

Résumé

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.
Publiée
2017-10-13
Rubrique
Numéro spécial : Statistique pour les données spatiales et spatio-temporelles et réseau RESSTE