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On least squares fitting for stationary spatial point processes

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The K-function is a popular tool for fitting spatial point process models owing to its simplicity and wide applicability. In this work we study the properties of least squares estimators of model parameters and propose a new method of model fitting via the K-function by using subsampling. We demonstrate consistency and asymptotic normality of our estimators of model parameters and compare the efficiency of our procedure with existing procedures. This is done through asymptotic theory, simulation experiments and an application to a data set on long leaf pine-trees.
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Keywords: K-function; Least squares estimator; Spatial point process; Subsampling

Document Type: Research Article

Affiliations: 1: Yale University, New Haven, USA 2: Texas A&M University, College Station, USA

Publication date: 2007-02-01

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