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Nonparametric identification for respondent-driven sampling

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  • Aronow, Peter M.
  • Crawford, Forrest W.

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.

Suggested Citation

  • Aronow, Peter M. & Crawford, Forrest W., 2015. "Nonparametric identification for respondent-driven sampling," Statistics & Probability Letters, Elsevier, vol. 106(C), pages 100-102.
    • Handle: RePEc:eee:stapro:v:106:y:2015:i:c:p:100-102
    DOI: 10.1016/j.spl.2015.07.003
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    References listed on IDEAS

    as
    1. Gile, Krista J., 2011. "Improved Inference for Respondent-Driven Sampling Data With Application to HIV Prevalence Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 135-146.
    2. Krista J. Gile & Lisa G. Johnston & Matthew J. Salganik, 2015. "Diagnostics for respondent-driven sampling," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(1), pages 241-269, January.
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