A tutorial on estimator averaging in spatial point process models

TL;DR

This paper provides an accessible overview of estimator averaging in spatial point process models, demonstrating its effectiveness in improving estimation accuracy over standard methods with simple implementation in R.

Contribution

It offers a clear tutorial on estimator averaging, including performance evaluation and practical R code for spatial point process models.

Findings

Average estimator outperforms standard procedures

Method improves estimation accuracy in tested models

Implementation is simple with few lines of R code

Abstract

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.