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    Rights statement: This is the author’s version of a work that was accepted for publication in Spatial Statistics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial Statistics, 28, 2018 DOI: 10.1016/j.spasta.2018.06.004

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

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>12/2018
<mark>Journal</mark>Spatial Statistics
Volume28
Number of pages12
Pages (from-to)319-330
Publication statusPublished
Early online date23/06/18
Original languageEnglish

Abstract

In almost any geostatistical analysis, one of the underlying, often implicit, modelling assumptions 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 privacy 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 overcomes 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 outperforms 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. We conclude that our approach based on the composite likelihood is a feasible and computationally more efficient alternative option to existing likelihood-based methods that deal with positional error in a geostatistical context.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Spatial Statistics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial Statistics, 28, 2018 DOI: 10.1016/j.spasta.2018.06.004