A least-squares cross-validation bandwidth selection approach in pair correlation function estimations

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

The pair correlation function is a useful tool to analyze spatial point patterns. It is often estimated nonparametrically by a procedure such as kernel smoothing. This article develops a data-driven method for the selection of the bandwidth involved in the estimation. The proposed method uses the idea of least-squares cross-validation which has been often applied for bandwidth selection in density estimation and many other nonparametric estimations. The asymptotic property of the proposed approach will be investigated under an increasing-domain setting in this paper.

Original languageEnglish (US)
Pages (from-to)1722-1729
Number of pages8
JournalStatistics and Probability Letters
Volume77
Issue number18
DOIs
StatePublished - Dec 2007
Externally publishedYes

Fingerprint

Bandwidth Selection
Pair Correlation Function
Function Estimation
Cross-validation
Least Squares
Spatial Point Pattern
Kernel Smoothing
Density Estimation
Nonparametric Estimation
Data-driven
Asymptotic Properties
Bandwidth
Least squares
Density estimation
Nonparametric estimation
Asymptotic properties
Kernel smoothing

Keywords

  • Bandwidth selection
  • Least-squares cross-validation
  • Pair correlation function

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Statistics and Probability

Cite this

A least-squares cross-validation bandwidth selection approach in pair correlation function estimations. / Guan, Yongtao.

In: Statistics and Probability Letters, Vol. 77, No. 18, 12.2007, p. 1722-1729.

Research output: Contribution to journalArticle

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