Zero-inflated modelling for characterizing coverage errors of extracts from the US Census Bureau's Master Address File

Derek S. Young, Andrew M. Raim, Nancy R. Johnson

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


To meet the strategic goals and objectives for the 2020 census, the US Census Bureau must make fundamental changes to the design, implementation and management of the decennial census. The changes must build on the successes and address the challenges of the previous censuses. Of particular interest is to gauge the on-going quality of the census frames. We address this topic by discussing a set of statistical models for the Master Address File that will produce estimates of coverage error at levels of geography down to the block level. The distributions of added and deleted housing units in a block are used to characterize the undercoverage and overcoverage respectively. The data used are from the 2010 address canvassing operation. As will be shown, these distributions are highly right skewed with a very large proportion of 0 counts. Hence, we utilize zero-inflated regression modelling to determine the predicted distribution of additions and deletions. In addition to standard statistical measures, we gauge the performance of this model by simulating a 2010 address canvassing operation using a specified coverage level. We also discuss future maintenance and updating of this model.

Original languageEnglish
Pages (from-to)73-97
Number of pages25
JournalJournal of the Royal Statistical Society. Series A: Statistics in Society
Issue number1
StatePublished - Jan 1 2017

Bibliographical note

Publisher Copyright:
© 2016 The Authors Journal of the Royal Statistical Society: Series A (Statistics in Society) Published by John Wiley & Sons Ltd on behalf of the Royal Statistical Society.


  • Coverage
  • Cross-validation
  • Negative binomial
  • Sample survey frame improvement
  • Variable selection
  • Zero-inflated counts

ASJC Scopus subject areas

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty


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