Nonparametric estimation of spatial segregation in a multivariate point process: Bovine tuberculosis in Cornwall, UK

Peter Diggle, Pingping Zheng, Peter Durr

Research output: Contribution to journalArticlepeer-review

Abstract

The paper is motivated by a problem in veterinary epidemiology, in which spatially referenced breakdowns of bovine tuberculosis are classified according to their genotype and year of occurrence. We develop a nonparametric method for addressing spatial segregation in the resulting multivariate spatial point process, with associated Monte Carlo tests for the null hypothesis that different genotypes are randomly intermingled and no temporal changes in spatial segregation. Our spatial segregation estimates use a kernel regression method with bandwidth selected by a multivariate cross-validated likelihood criterion.

Original languageEnglish (US)
Pages (from-to)645-658
Number of pages14
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
Volume54
Issue number3
DOIs
StatePublished - 2005
Externally publishedYes

Keywords

  • Bovine tuberculosis
  • Monte Carlo test
  • Multivariate point process
  • Spatial segregation

ASJC Scopus subject areas

  • Mathematics(all)
  • Statistics and Probability

Fingerprint Dive into the research topics of 'Nonparametric estimation of spatial segregation in a multivariate point process: Bovine tuberculosis in Cornwall, UK'. Together they form a unique fingerprint.

Cite this