A tutorial on estimator averaging in spatial point process models
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Publication:4594511
zbMATH Open1378.62094arXiv1607.00864MaRDI QIDQ4594511
Frédéric Lavancier, Paul Rochet
Publication date: 24 November 2017
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.
Full work available at URL: https://arxiv.org/abs/1607.00864
Inference from spatial processes (62M30) Point estimation (62F10) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)
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