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The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
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Consistency for the tree bootstrap in respondent-driven sampling.

A K B Green1, T H McCormick2, A E Raftery2

  • 1Scottish Government, Atlantic Quay, 150 Broomielaw, Glasgow G2 8LU, UK.

Biometrika
|May 27, 2020
PubMed
Summary
This summary is machine-generated.

Respondent-driven sampling (RDS) helps study hidden populations. This research confirms the consistency of the tree bootstrap method for uncertainty estimation in RDS, improving population proportion estimates.

Keywords:
Block bootstrapConsistencyRespondent-driven samplingTree bootstrap

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Area of Science:

  • Statistics
  • Epidemiology
  • Social Sciences

Background:

  • Respondent-driven sampling (RDS) is crucial for surveying hard-to-reach populations, such as injection drug users.
  • Estimating uncertainty in RDS is vital for accurate population proportion analysis.
  • Previous work introduced the tree bootstrap method for RDS uncertainty estimation.

Purpose of the Study:

  • To establish the consistency of the tree bootstrap method for uncertainty estimation in Respondent-driven sampling.
  • To validate the statistical rigor of tree bootstrap for [Formula: see text]-trees in RDS.

Main Methods:

  • Utilizing theoretical statistical analysis.
  • Applying the tree bootstrap method to [Formula: see text]-tree structures within RDS data.
  • Proving the consistency of the estimation approach.

Main Results:

  • The consistency of the tree bootstrap approach for [Formula: see text]-trees in Respondent-driven sampling was mathematically established.
  • This confirms the reliability of the method for uncertainty estimation in complex RDS networks.

Conclusions:

  • The tree bootstrap method provides a statistically sound approach for estimating uncertainty in population proportion estimates derived from Respondent-driven sampling.
  • This finding enhances the utility of RDS for epidemiological and social science research involving hidden populations.