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dc.contributor.authorKarra, Maheshen_US
dc.contributor.authorCanning, Daviden_US
dc.contributor.authorSato, Ryokoen_US
dc.date.accessioned2021-02-05T14:23:14Z
dc.date.available2021-02-05T14:23:14Z
dc.date.issued2020-11
dc.identifier.citationMahesh Karra, David Canning, Ryoko Sato. 2020. "Adding measurement error to location data to protect subject confidentiality while allowing for consistent estimation of exposure effects." Journal of the Royal Statistical Society: Series C (Applied Statistics), Volume 69, Issue 5, pp. 1251 - 1268. https://doi.org/10.1111/rssc.12439
dc.identifier.issn0035-9254
dc.identifier.issn1467-9876
dc.identifier.urihttps://hdl.handle.net/2144/41989
dc.description.abstractIn public use data sets, it is desirable not to report a respondent's location precisely to protect subject confidentiality. However, the direct use of perturbed location data to construct explanatory exposure variables for regression models will generally make naive estimates of all parameters biased and inconsistent. We propose an approach where a perturbation vector, consisting of a random distance at a random angle, is added to a respondent's reported geographic co‐ordinates. We show that, as long as the distribution of the perturbation is public and there is an underlying prior population density map, external researchers can construct unbiased and consistent estimates of location‐dependent exposure effects by using numerical integration techniques over all possible actual locations, although coefficient confidence intervals are wider than if the true location data were known. We examine our method by using a Monte Carlo simulation exercise and apply it to a real world example using data on perceived and actual distance to a health facility in Tanzania.en_US
dc.format.extentp. 1251 - 1268en_US
dc.languageen
dc.language.isoen_US
dc.publisherWileyen_US
dc.relation.ispartofJournal of the Royal Statistical Society: Series C (Applied Statistics)
dc.rights© 2020 The Authors. Journal of the Royal Statistical Society: Series C (Applied Statistics) Published by John Wiley & Sons Ltd on behalf of the Royal Statistical Society. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.en_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/.
dc.subjectStatistics & probabilityen_US
dc.subjectStatisticsen_US
dc.subjectData privacyen_US
dc.subjectMeasurement erroren_US
dc.subjectMonte Carlo simulationen_US
dc.subjectNumerical integrationen_US
dc.subjectTanzaniaen_US
dc.titleAdding measurement error to location data to protect subject confidentiality while allowing for consistent estimation of exposure effectsen_US
dc.typeArticleen_US
dc.description.versionPublished versionen_US
dc.identifier.doi10.1111/rssc.12439
pubs.elements-sourcecrossrefen_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, Frederick S. Pardee School of Global Studiesen_US
pubs.publication-statusPublisheden_US
dc.date.online2020-08-15
dc.identifier.orcid0000-0003-0962-092X (Karra, Mahesh)
dc.identifier.mycv585851


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© 2020 The Authors. Journal of the Royal Statistical Society: Series C (Applied Statistics) Published by John Wiley & Sons Ltd on behalf of the Royal Statistical Society. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
Except where otherwise noted, this item's license is described as © 2020 The Authors. Journal of the Royal Statistical Society: Series C (Applied Statistics) Published by John Wiley & Sons Ltd on behalf of the Royal Statistical Society. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.