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Statistics & probability letters
Nov 01, 2015;106 100-102.

Nonparametric Identification for Respondent-Driven Sampling.

Peter M Aronow, Forrest W Crawford
Author Information
  1. Peter M Aronow: Department of Political Science, Yale University ; Department of Biostatistics, Yale School of Public Health.
  2. Forrest W Crawford: Department of Biostatistics, Yale School of Public Health.
PMID: 26327739 DOI: 10.1016/j.spl.2015.07.003

Abstract

We detail nonparametric identification results for respondent-driven sampling when sampling probabilities are assumed to be functions of network degree known to scale. We show that the conditions for consistency of the Volz-Heckathorn estimator are weaker than previously assumed.

Keywords

Horvitz-Thompson estimator network degree respondent-driven sampling

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Grants

  1. KL2 TR000140/NCATS NIH HHS
  2. P30 MH062294/NIMH NIH HHS
Journal Article
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Created with Highcharts 10.0.0samplingrespondent-drivenassumednetworkdegreeestimatordetailnonparametricidentificationresultsprobabilitiesfunctionsknownscaleshowconditionsconsistencyVolz-HeckathornweakerpreviouslyNonparametricIdentificationRespondent-DrivenSamplingHorvitz-Thompson

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