Comparing four bootstrap methods for stratified three-stage sampling

Hiroshi Saigo*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


For a stratified three-stage sampling design with simple random sampling without replacement at each stage, only the Bernoulli bootstrap is currently available as a bootstrap for design-based inference under arbitrary sampling fractions. This article extends three other methods (the mirror-match bootstrap, the rescaling bootstrap, and the without-replacement bootstrap) to the design and conducts simulation study that estimates variances and constructs coverage intervals for a population total and selected quantiles. The without-replacement bootstrap proves the least biased of the four methods when estimating the variances of quantiles. Otherwise, the methods are comparable.

Original languageEnglish
Pages (from-to)193-207
Number of pages15
JournalJournal of Official Statistics
Issue number1
Publication statusPublished - 2010 Mar 1


  • High sampling fractions
  • Multistage sampling
  • Quantile estimation
  • Resampling methods

ASJC Scopus subject areas

  • Statistics and Probability


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