Combining Euclidean and composite likelihood for binary spatial data estimation

Moreno Bevilacqua, Federico Crudu, Emilio Porcu

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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.

Original languageBritish English
Pages (from-to)335-346
Number of pages12
JournalStochastic Environmental Research and Risk Assessment
Issue number2
StatePublished - Feb 2014


Dive into the research topics of 'Combining Euclidean and composite likelihood for binary spatial data estimation'. Together they form a unique fingerprint.

Cite this