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Estimating the size of populations at high risk for HIV using respondent-driven sampling data

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  • Mark S. Handcock
  • Krista J. Gile
  • Corinne M. Mar

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Suggested Citation

  • Mark S. Handcock & Krista J. Gile & Corinne M. Mar, 2015. "Estimating the size of populations at high risk for HIV using respondent-driven sampling data," Biometrics, The International Biometric Society, vol. 71(1), pages 258-266, March.
    • Handle: RePEc:bla:biomet:v:71:y:2015:i:1:p:258-266
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    File URL: http://hdl.handle.net/10.1111/biom.12255
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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. West, Mike, 1996. "Inference in successive sampling discovery models," Journal of Econometrics, Elsevier, vol. 75(1), pages 217-238, November.
    3. S. E. Fienberg & M. S. Johnson & B. W. Junker, 1999. "Classical multilevel and Bayesian approaches to population size estimation using multiple lists," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 162(3), pages 383-405.
    4. Galit Shmueli & Thomas P. Minka & Joseph B. Kadane & Sharad Borle & Peter Boatwright, 2005. "A useful distribution for fitting discrete data: revival of the Conway–Maxwell–Poisson distribution," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(1), pages 127-142, January.
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    Cited by:

    1. Ian E. Fellows & Mark S. Handcock, 2023. "Modeling of networked populations when data is sampled or missing," METRON, Springer;Sapienza Università di Roma, vol. 81(1), pages 21-35, April.

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