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Combining Euclidean and composite likelihood for binary spatial data estimation

  • Moreno Bevilacqua
  • Federico CruduEmail author
  • Emilio Porcu
Original Paper

Abstract

In this paper we propose a blockwise Euclidean likelihood method for the estimation of a spatial binary field obtained by thresholding a latent Gaussian random field. The moment conditions used in the Euclidean likelihood estimator derive from the score of the composite likelihood based on marginal pairs. A feature of this approach is that it is possible to obtain computational benefits with respect to the pairwise likelihood depending on the choice of the spatial blocks. A simulation study and an analysis on cancer mortality data compares the two methods in terms of statistical and computational efficiency. We also study the asymptotic properties of the proposed estimator.

Keywords

Binary spatial fields Latent gaussian fields Euclidean likelihood Composite likelihood 

Notes

Acknowledgments

Research work of Moreno Bevilacqua was partially supported by grant FONDECYT 11121408 from the Chilean government. Research work of Emilio Porcu is supported by Proyecto Fondecyt Regular N. 1130647 from the Chilean Government. We are indebted with Prof. Daniel Nordman for having provided us with the cancer mortality data.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Moreno Bevilacqua
    • 1
  • Federico Crudu
    • 2
    Email author
  • Emilio Porcu
    • 3
  1. 1.Departamento de EstadísticaUniversidad de ValparaisoValparaisoChile
  2. 2.Instituto de EstadísticaPontificia Universidad Católica de ValparaísoValparaisoChile
  3. 3.Departamento de MatemáticasUniversidad Técnica Federico Santa MariaValparaisoChile

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