KERNEL METHOD FOR SMOOTHING POINT PROCESS DATA.

Peter Diggle

Research output: Contribution to journalArticle

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

Fingerprint

Kernel Methods
Point Process
Smoothing
End Effect
Cox Process
Estimator
Density Estimation
Stationary Process
Probability Density
Mean Squared Error
Point process
Kernel methods
Cox process
Density estimation
Seam
Mean squared error

ASJC Scopus subject areas

  • Mathematics(all)
  • Statistics and Probability

Cite this

KERNEL METHOD FOR SMOOTHING POINT PROCESS DATA. / Diggle, Peter.

In: Journal of the Royal Statistical Society. Series C: Applied Statistics, Vol. 34, No. 2, 1985, p. 138-147.

Research output: Contribution to journalArticle

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