2016/07/04 by Frédéric Lavancier, Lavancier, Frédéric, Paul Rochet +1
Economics, Econometrics and Finance · Environmental Science · Mathematics · #FOS: Mathematics #Point processes and geometric inequalities #Soil Geostatistics and Mapping #Spatial and Panel Data Analysis #Statistics Theory (math.ST)
paper · pdf · doi:10.48550/arxiv.1607.00864
openalex publication_date 2016/07/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.