A KPSS test for stationarity for spatial point processes

Research output: Contribution to journalArticle

15 Scopus citations

Abstract

We propose a formal method to test stationarity for spatial point processes. The proposed test statistic is based on the integrated squared deviations of observed counts of events from their means estimated under stationarity. We show that the resulting test statistic converges in distribution to a functional of a two-dimensional Brownian motion. To conduct the test, we compare the calculated statistic with the upper tail critical values of this functional. Our method requires only a weak dependence condition on the process but does not assume any parametric model for it. As a result, it can be applied to a wide class of spatial point process models. We study the efficacy of the test through both simulations and applications to two real data examples that were previously suspected to be nonstationary based on graphical evidence. Our test formally confirmed the suspected nonstationarity for both data.

Original languageEnglish (US)
Pages (from-to)800-806
Number of pages7
JournalBiometrics
Volume64
Issue number3
DOIs
StatePublished - Sep 1 2008
Externally publishedYes

Keywords

  • KPSS test for stationarity
  • Spatial point process

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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