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On least squares fitting for stationary spatial point processes  [2007]

Guan, Yongtao Sherman, Michael

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Abstract
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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Other subjects

  • K-function
  • Spatial point process
  • Least squares estimator
  • Subsampling

From the journal

Journal of the Royal Statistical Society. Series B, Statistical methodology

ISSN : 1369-7412

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Other information

Language : English

Type : Journal Article

In AGRIS since : 2013

Volume : 69

Issue : 1

Start Page : 31

End Page : 49

Publisher : Oxford, UK : Blackwell Publishing Ltd

All titles :

" On least squares fitting for stationary spatial point processes "