KERNEL METHOD FOR SMOOTHING POINT PROCESS DATA.

Peter Diggle

Research output: Contribution to journalArticlepeer-review

Abstract

A method for estimating the local intensity of a one-dimensional point process is described. The estimator uses an adaptation of Rosenblatt's kernel method of non-parametric probability density estimation, with a correction for end-effects. An expression for the mean squared error is derived on the assumption that the underlying process is a stationary Cox process, and this result is used to suggest a practical method for choosing the value of the smoothing constant. The performance of the estimator is illustrated using simulated data. An application to data on the locations of joints along a coal seam is described. The extension to two-dimensional point processes is noted.

Original languageEnglish (US)
Pages (from-to)138-147
Number of pages10
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
Volume34
Issue number2
StatePublished - 1985
Externally publishedYes

ASJC Scopus subject areas

  • Mathematics(all)
  • Statistics and Probability

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