On least squares fitting for stationary spatial point processes

Guan Yongtao, Michael Sherman

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)31-49
Number of pages19
JournalJournal of the Royal Statistical Society. Series B: Statistical Methodology
Issue number1
StatePublished - Feb 1 2007
Externally publishedYes


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

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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