  In almost any geostatistical analysis, one of the underlying, often implicit,
modelling assump- tions is that the spatial locations, where measurements are
taken, are recorded without error. In this study we develop geostatistical
inference when this assumption is not valid. This is often the case when, for
example, individual address information is randomly altered to provide pri-
vacy protection or imprecisions are induced by geocoding processes and
measurement devices. Our objective is to develop a method of inference based on
the composite likelihood that over- comes the inherent computational limits of
the full likelihood method as set out in Fanshawe and Diggle (2011). Through a
simulation study, we then compare the performance of our proposed approach with
an N-weighted least squares estimation procedure, based on a corrected version
of the empirical variogram. Our results indicate that the composite-likelihood
approach outper- forms the latter, leading to smaller root-mean-square-errors
in the parameter estimates. Finally, we illustrate an application of our method
to analyse data on malnutrition from a Demographic and Health Survey conducted
in Senegal in 2011, where locations were randomly perturbed to protect the
privacy of respondents.
