A generalization of the neyman-scott process

Chun Yip Yau, Ji Meng Loh

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

In this paper we introduce a generalization of the Neyman-Scott process Neyman and Scott (1958) that allows for regularity in the parent process. In particular, we consider the special case where the parent process is a Strauss process with offspring points dispersed about the parent points. Such a generalization allows for point realizations that show a mix of regularity and clustering in the points. We work out a closed form approximation of the K function for this model and use this to fit the model to data. The approach is illustrated by applications to the locations of a species of trees in a rainforest dataset.

Original languageEnglish (US)
Pages (from-to)1717-1736
Number of pages20
JournalStatistica Sinica
Volume22
Issue number4
DOIs
StatePublished - Oct 1 2012
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

  • Gibbs process
  • K-function
  • Neyman-Scott process
  • Regular point process

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