Combining Euclidean and composite likelihood for binary spatial data estimation

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

Marginal Logistic Regression for Spatially Clustered Binary Data

“…Bevilacqua et al . () also considered an empirical lorelogram to characterize the spatial association between binary data in a probit spatial model with an exponential correlation function; see also the R implementation in the package CompRandFld (Padoan and Bevilacqua, ). However, our empirical lorelogram differs substantially from that of Bevilacqua et al .…”

“…However, our empirical lorelogram differs substantially from that of Bevilacqua et al . () because our proposal is specifically designed to describe and model the residual spatial dependence in marginal logistic regression.…”