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

Geostatistical inference in the presence of geomasking: a composite-likelihood approach