Show simple item record

dc.date.accessioned2018-06-14T09:31:18Z
dc.date.issued2007-11
dc.identifierhttp://publications.icr.ac.uk/5388/
dc.identifier.citationJOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, 2007, 56 (5), pp. 551 - 570
dc.identifier.issn0035-9254
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/1869
dc.description.abstractWe propose an approach for assessing the risk of individual identification in the release of categorical data. This requires the accurate calculation of predictive probabilities for those cells in a contingency table which have small sample frequencies, making the problem somewhat different from usual contingency table estimation, where interest is generally focused on regions of high probability. Our approach is Bayesian and provides posterior predictive probabilities of identification risk. By incorporating model uncertainty in our analysis, we can provide more realistic estimates of disclosure risk for individual cell counts than are provided by methods which ignore the multivariate structure of the data set.
dc.format.extent551 - 570
dc.languageeng
dc.language.isoeng
dc.subjectcategorical data; identification; model uncertainty; prediction GRAPHICAL MODELS; MICRODATA
dc.titleBayesian disclosure risk assessment: predicting small frequencies in contingency tables
dc.typeJournal Article
rioxxterms.licenseref.startdate2007-11
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfJOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS
pubs.issue5
pubs.notesNot known
pubs.organisational-group/ICR
pubs.organisational-group/ICR
pubs.volume56
pubs.embargo.termsNot known
dc.contributor.icrauthorWebb, Emilyen


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following collection(s)

Show simple item record