Literature DB >> 32454530

Consistency for the tree bootstrap in respondent-driven sampling.

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

Abstract

Respondent-driven sampling is an approach for estimating features of populations that are difficult to access using standard survey tools, e.g., the fraction of injection drug users who are HIV positive. Baraff et al. (2016) introduced an approach to estimating uncertainty in population proportion estimates from respondent-driven sampling using the tree bootstrap method. In this paper we establish the consistency of this tree bootstrap approach in the case of [Formula: see text]-trees.
© 2020 Biometrika Trust.

Entities:  

Keywords:  Block bootstrap; Consistency; Respondent-driven sampling; Tree bootstrap

Year:  2020        PMID: 32454530      PMCID: PMC7228542          DOI: 10.1093/biomet/asz067

Source DB:  PubMed          Journal:  Biometrika        ISSN: 0006-3444            Impact factor:   2.445


  6 in total

1.  Assessing respondent-driven sampling.

Authors:  Sharad Goel; Matthew J Salganik
Journal:  Proc Natl Acad Sci U S A       Date:  2010-03-29       Impact factor: 11.205

2.  Estimating uncertainty in respondent-driven sampling using a tree bootstrap method.

Authors:  Aaron J Baraff; Tyler H McCormick; Adrian E Raftery
Journal:  Proc Natl Acad Sci U S A       Date:  2016-12-07       Impact factor: 11.205

3.  THE GRAPHICAL STRUCTURE OF RESPONDENT-DRIVEN SAMPLING.

Authors:  Forrest W Crawford
Journal:  Sociol Methodol       Date:  2016-08-01

4.  Respondent-Driven Sampling: An Assessment of Current Methodology.

Authors:  Krista J Gile; Mark S Handcock
Journal:  Sociol Methodol       Date:  2010-08

5.  Respondent-driven sampling as Markov chain Monte Carlo.

Authors:  Sharad Goel; Matthew J Salganik
Journal:  Stat Med       Date:  2009-07-30       Impact factor: 2.373

6.  Network Model-Assisted Inference from Respondent-Driven Sampling Data.

Authors:  Krista J Gile; Mark S Handcock
Journal:  J R Stat Soc Ser A Stat Soc       Date:  2015-01-27       Impact factor: 2.483

  6 in total

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