Nonparametric variogram and covariogram estimation with Fourier-Bessel matrices

Marc Genton*, David J. Gorsich

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

The nonparametric estimation of variograms and covariograms for isotropic stationary spatial stochastic processes is considered. It is shown that Fourier-Bessel matrices play an important role in this context because they provide an orthogonal discretization of the spectral representation of positive definite functions. Their properties are investigated and an example from a simulated two-dimensional spatial process is provided. It is shown that this approach provides a smooth and positive definite nonparametric estimator in the continuum, whereas previous methods typically suffer from spurious oscillations. A practical example from Astronomy is used for illustration.

Original languageEnglish (US)
Pages (from-to)47-57
Number of pages11
JournalComputational Statistics and Data Analysis
Volume41
Issue number1
DOIs
StatePublished - Nov 28 2002

Keywords

  • Discretization
  • Fourier-Bessel expansion
  • Kriging
  • Nonnegative least squares
  • Orthogonality
  • Positive definiteness
  • Spatial statistics

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

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