Bootstrapping an inhomogeneous point process

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Abstract

In this paper, we focus on resampling non-stationary weakly dependent point processes in two dimensions to make inference on the inhomogeneous K function (Baddeley et al., 2000). We provide theoretical results that show a consistency result of the bootstrap estimates of the variance as the observation region and resampling blocks increase in size. We present results of a simulation study that examines the performance of nominal 95% confidence intervals for the inhomogeneous K function obtained via our bootstrap procedure. The procedure is also applied to a rainforest dataset.

Original languageEnglish (US)
Pages (from-to)734-749
Number of pages16
JournalJournal of Statistical Planning and Inference
Volume140
Issue number3
DOIs
StatePublished - Mar 2010
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

Keywords

  • Inhomogeneous K function
  • Inhomogeneous point process
  • Marked point bootstrap
  • Spatial bootstrap

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