Have a personal or library account? Click to login
Combining Cluster Sampling and Link-Tracing Sampling to Estimate Totals and Means of Hidden Populations in Presence of Heterogeneous Probabilities of Links Cover

Combining Cluster Sampling and Link-Tracing Sampling to Estimate Totals and Means of Hidden Populations in Presence of Heterogeneous Probabilities of Links

Journal of Official Statistics
Volume 37 (2021): Issue 4 (December 2021)
By:
Open Access
|Dec 2021

References

  1. Abramowitz, M., and I.A. Stegun. 1964. <em>Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables</em>. Ninth Dover printing, tenth GPO printing. New York: Dover.
    Search in Google ScholarBack to article
  2. Bernard, H.R., T. Hallett, A. Iovita, E.C. Johnsen, R. Lyerla, C. McCarty, M. Mahy, M.J. Salganik, T. Saliuk, O. Scutelniciuc, G.A. Shelley, P. Sirinirund, S. Weir, and D.F. Stroup. 2010. “Counting hard-to-count populations: The network scale-up method for public health.” <em>Sexually Transmitted Infections</em> 86 (Suppl. 2): 11–15. DOI: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://dx.doi.org/<a href="https://doi.org/10.1136/sti.2010.044446" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1136/sti.2010.044446</a>">http://dx.doi.org/<a href="https://doi.org/10.1136/sti.2010.044446" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1136/sti.2010.044446</a></ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1136/sti.2010.044446" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1136/sti.2010.044446</a></dgdoi:pub-id><dgpm:pub-id xmlns:dgpm="http://degruyter.com/resources/fetched-pubmed-id" pub-id-type="pmcid">3010902</dgpm:pub-id><dgpm:pub-id xmlns:dgpm="http://degruyter.com/resources/fetched-pubmed-id" pub-id-type="pmid">21106509</dgpm:pub-id>
    Search in Google ScholarBack to article
  3. Booth, J.G., R.W. Butler, and P. Hall. 1994. “Bootstrap methods for finite populations.” <em>Journal of the American Statistical Association</em> 89: 1282 – 1289. DOI: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1080/01621459.1994.10476868" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1080/01621459.1994.10476868</a>">https://doi.org/10.1080/01621459.1994.10476868</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1080/01621459.1994.10476868" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1080/01621459.1994.10476868</a></dgdoi:pub-id>
    Search in Google ScholarBack to article
  4. Burnham, K.P., and W.S. Overton. 1978. “Estimation of the size of a closed population when capture probabilities vary among animals.” <em>Biometrika</em> 65: 625–633. DOI: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1093/biomet/65.3.625" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1093/biomet/65.3.625</a>">https://doi.org/10.1093/biomet/65.3.625</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1093/biomet/65.3.625" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1093/biomet/65.3.625</a></dgdoi:pub-id>
    Search in Google ScholarBack to article
  5. Chao, A. 1987. “Estimating the population size for capture-recapture data with unequal catchability.” <em>Biometrics</em> 43: 783–791. DOI: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.2307/2531532" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.2307/2531532</a>">https://doi.org/10.2307/2531532</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.2307/2531532" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.2307/2531532</a></dgdoi:pub-id>
    Search in Google ScholarBack to article
  6. Cheng, S., D.J. Eck, and F.W. Crawford. 2020. “Estimating the size of a hidden finite set: Large-sample behavior of estimators.” <em>Statistics Surveys</em> 14: 1–31. DOI: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1214/19-SS127" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1214/19-SS127</a>">https://doi.org/10.1214/19-SS127</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1214/19-SS127" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1214/19-SS127</a></dgdoi:pub-id>
    Search in Google ScholarBack to article
  7. Chow, M., and S.K. Thompson. 2003. “Estimation with link-tracing sampling designs – A Bayesian approach.” <em>Survey Methodology</em> 29: 197–205. Available at: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2006002/article/9553-eng.pdf?st=6cXXjDD2">https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2006002/article/9553-eng.pdf?st=6cXXjDD2</ext-link> (accessed April 2020).
    Search in Google ScholarBack to article
  8. Coull, B.A., and A. Agresti. 1999. “The use of mixed logit models to reflect heterogeneity in capture-recapture studies.” <em>Biometrics</em> 55: 294–301. DOI: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1111/j.0006-341X.1999.00294.x" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1111/j.0006-341X.1999.00294.x</a>">https://doi.org/10.1111/j.0006-341X.1999.00294.x</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1111/j.0006-341X.1999.00294.x" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1111/j.0006-341X.1999.00294.x</a></dgdoi:pub-id>
    Search in Google ScholarBack to article
  9. Crawford, F.W., J. Wu, and R. Heimer. 2018. “Hidden population size estimation from respondent-driven sampling: A network approach.” <em>Journal of the American Statistical Association</em> 113: 755–766. DOI: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1080/01621459.2017.1285775" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1080/01621459.2017.1285775</a>">https://doi.org/10.1080/01621459.2017.1285775</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1080/01621459.2017.1285775" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1080/01621459.2017.1285775</a></dgdoi:pub-id><dgpm:pub-id xmlns:dgpm="http://degruyter.com/resources/fetched-pubmed-id" pub-id-type="pmcid">6392194</dgpm:pub-id><dgpm:pub-id xmlns:dgpm="http://degruyter.com/resources/fetched-pubmed-id" pub-id-type="pmid">30828120</dgpm:pub-id>
    Search in Google ScholarBack to article
  10. Dávid, B., and T.A.B. Snijders. 2002. “Estimating the Size of the Homeless Population in Budapest, Hungary.” <em>Quality &amp; Quantity</em> 36: 291–303. DOI: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1023/A:1016080606287" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1023/A:1016080606287</a>">https://doi.org/10.1023/A:1016080606287</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1023/A:1016080606287" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1023/A:1016080606287</a></dgdoi:pub-id>
    Search in Google ScholarBack to article
  11. Davison, A.C., and D.V. Hinkley. 1997. <em>Bootstrap Methods and their Applications</em>. New York: Cambridge University Press.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1017/CBO9780511802843" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1017/CBO9780511802843</a></dgdoi:pub-id>
    Search in Google ScholarBack to article
  12. Dombrowski, K., B. Khan, T. Wendel, K. McLean, E. Misshula, and R. Curtis. 2012. “Estimating the Size of the Methamphetamine-Using Population in New York City Using Network Sampling Techniques.” <em>Advances in Applied Sociology</em> 2: 245–252. DOI: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.4236/aasoci.2012.24032" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.4236/aasoci.2012.24032</a>">https://doi.org/10.4236/aasoci.2012.24032</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.4236/aasoci.2012.24032" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.4236/aasoci.2012.24032</a></dgdoi:pub-id><dgpm:pub-id xmlns:dgpm="http://degruyter.com/resources/fetched-pubmed-id" pub-id-type="pmcid">3963163</dgpm:pub-id><dgpm:pub-id xmlns:dgpm="http://degruyter.com/resources/fetched-pubmed-id" pub-id-type="pmid">24672746</dgpm:pub-id>
    Search in Google ScholarBack to article
  13. Félix-Medina, M.H., and P.E. Monjardin. 2006. “Combining link-tracing sampling and cluster sampling to estimate the size of hidden populations: A Bayesian assisted approach.” <em>Survey Methodology</em> 32: 187–195. Available at: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2006002/article/9553-eng.pdf?st=6cXXjDD2">https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2006002/article/9553-eng.pdf?st=6cXXjDD2</ext-link> (accessed April 2020).
    Search in Google ScholarBack to article
  14. Félix-Medina, M.H., and P.E. Monjardin. 2010. “Combining Link-Tracing Sampling and Cluster Sampling to Estimate Totals and Means of Hidden Human Populations.” <em>Journal of Official Statistics</em> 26(4): 603–631. Available at: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://www.scb.se/contentassets/ca21efb41fee47d293bbee5bf7be7fb3/20200206/felix-medina.pdf">https://www.scb.se/contentassets/ca21efb41fee47d293bbee5bf7be7fb3/20200206/felix-medina.pdf</ext-link> (accessed April 2020).
    Search in Google ScholarBack to article
  15. Félix-Medina, M.H., P.E. Monjardin, and A.N. Aceves-Castro. 2015. “Combining link-tracing sampling and cluster sampling to estimate the size of a hidden population in presence of heterogeneous link-probabilities.” <em>Survey Methodology</em>: 349–376. Available at: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2015002/article/14238-eng.pdf?st=ki7rx2GZ">https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2015002/article/14238-eng.pdf?st=ki7rx2GZ</ext-link> (accessed April 2020).
    Search in Google ScholarBack to article
  16. Félix-Medina, M.H., and S.K. Thompson. 2004. “Combining Cluster Sampling and Link-Tracing Sampling to Estimate the Size of Hidden Populations.” <em>Journal of Official Statistics</em> 20(1): 19–38. Available at: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://www.scb.se/contentassets/ca21efb41-fee47d293bbee5bf7be7fb3/combining-link-tracing-sampling-and-cluster-sampling-to-estimate-the-size-of-hidden-populations.pdf">https://www.scb.se/contentassets/ca21efb41-fee47d293bbee5bf7be7fb3/combining-link-tracing-sampling-and-cluster-sampling-to-estimate-the-size-of-hidden-populations.pdf</ext-link> (accessed April 2020).
    Search in Google ScholarBack to article
  17. Fienberg, S.E., M.S. Johnson, and B.W. Junker. 1999. “Classical multilevel and Bayesian approaches to population size estimation using multiple lists.” <em>Journal of the Royal Statistical Society. Series A</em> 162: 383–405. DOI: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1111/1467-985X.00143" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1111/1467-985X.00143</a>">https://doi.org/10.1111/1467-985X.00143</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1111/1467-985X.00143" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1111/1467-985X.00143</a></dgdoi:pub-id>
    Search in Google ScholarBack to article
  18. Frank, O., and T. Snijders. 1994. “Estimating the Size of Hidden Populations Using Snowball Sampling.” <em>Journal of Official Statistics</em> 10 (1): 53–67. Available at: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://www.scb.se/contentassets/ca21efb41fee47d293bbee5bf7be7fb3/estimating-the-size-of-hidden-populations-using-snowball-sampling.pdf">https://www.scb.se/contentassets/ca21efb41fee47d293bbee5bf7be7fb3/estimating-the-size-of-hidden-populations-using-snowball-sampling.pdf</ext-link> (accessed April 2020).
    Search in Google ScholarBack to article
  19. Freeman L.C. (n.d.) Network data sets repository. Available at: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://moreno.ss.uci.edu/data">http://moreno.ss.uci.edu/data</ext-link> (accessed June 2018).
    Search in Google ScholarBack to article
  20. Giner, G., and G.K. Smyth. 2016. “statmod: Probability calculations for the inverse Gaussian distribution.” <em>The R Journal</em> 8: 339–351. DOI: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.32614/RJ-2016-024" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.32614/RJ-2016-024</a>">https://doi.org/10.32614/RJ-2016-024</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.32614/RJ-2016-024" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.32614/RJ-2016-024</a></dgdoi:pub-id>
    Search in Google ScholarBack to article
  21. Handcock, M.S., K.J. Gile, and C.M. Mar. 2014. “Estimating hidden population size using respondent-driven sampling data.” <em>Electronic Journal of Statistics</em> 8: 1491–1521. DOI: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1214/14-EJS923" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1214/14-EJS923</a>">https://doi.org/10.1214/14-EJS923</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1214/14-EJS923" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1214/14-EJS923</a></dgdoi:pub-id>
    Search in Google ScholarBack to article
  22. Harris, K.M. 2013. “The add health study: Design and accomplishments.” Available at: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://www.cpc.unc.edu/projects/addhealth/data/guides/DesignPaperWIIV.pdf">www.cpc.unc.edu/projects/addhealth/data/guides/DesignPaperWIIV.pdf</ext-link> (accessed September 2017).
    Search in Google ScholarBack to article
  23. Heckathorn, D.D. 1997. “Respondent driven sampling: a new approach to the study of hidden samples.” <em>Social Problems</em> 44: 174–199. DOI: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.2307/3096941" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.2307/3096941</a>">https://doi.org/10.2307/3096941</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.2307/3096941" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.2307/3096941</a></dgdoi:pub-id>
    Search in Google ScholarBack to article
  24. Heckathorn, D.D. 2002. “Respondent-driven sampling II: deriving valid population estimates from chain-referral samples of hidden populations.” <em>Social Problems</em> 49: 11–34. DOI: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1525/sp.2002.49.1.11" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1525/sp.2002.49.1.11</a>">https://doi.org/10.1525/sp.2002.49.1.11</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1525/sp.2002.49.1.11" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1525/sp.2002.49.1.11</a></dgdoi:pub-id>
    Search in Google ScholarBack to article
  25. Heckathorn, D.D., and C.J. Cameron. 2017. “Network sampling: From snowball and multiplicity to respondent-driven sampling.” <em>Annual Review of Sociology</em> 43: 101–119. DOI: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1146/annurev-soc-060116-053556" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1146/annurev-soc-060116-053556</a>">https://doi.org/10.1146/annurev-soc-060116-053556</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1146/annurev-soc-060116-053556" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1146/annurev-soc-060116-053556</a></dgdoi:pub-id>
    Search in Google ScholarBack to article
  26. Hwang, W-H., and R. Huggins. 2005. “An examination of the effect of heterogeneity on the estimation of population size using capture-recapture data.” <em>Biometrika</em> 92: 229–233. DOI: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1093/biomet/92.1.229" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1093/biomet/92.1.229</a>">https://doi.org/10.1093/biomet/92.1.229</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1093/biomet/92.1.229" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1093/biomet/92.1.229</a></dgdoi:pub-id>
    Search in Google ScholarBack to article
  27. Johnston, L.G., D. Prybylski, H.F. Raymond, A. Mirzazadeh, C. Manopaiboon, and W. McFarland. 2013. “Incorporating the service multiplier method in respondent-driven sampling surveys to estimate the size of hidden and hard-to-reach populations: Case studies from around the world.” <em>Sexually Transmitted Diseases</em> 40: 304–310. DOI: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1097/OLQ.0b013e31827fd650" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1097/OLQ.0b013e31827fd650</a>">https://doi.org/10.1097/OLQ.0b013e31827fd650</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1097/OLQ.0b013e31827fd650" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1097/OLQ.0b013e31827fd650</a></dgdoi:pub-id>
    Search in Google ScholarBack to article
  28. Kalton, G. 2009. “Methods for oversampling rare populations in social surveys.” <em>Survey Methodology</em> 35: 125–141. Available at: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2009002/article/11036-eng.pdf?st=PyvQkTH4">https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2009002/article/11036-eng.pdf?st=PyvQkTH4</ext-link> (accessed April 2020).
    Search in Google ScholarBack to article
  29. Khan, B., H.-W. Lee, I. Fellows, and K. Dombrowski. 2018. “One-step estimation of networked population size: Respondent-driven capture-recapture with anonymity.” <em>PLoS ONE</em> 13 (4): 1–39. DOI: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1371/journal.pone.0195959" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1371/journal.pone.0195959</a>">https://doi.org/10.1371/journal.pone.0195959</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1371/journal.pone.0195959" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1371/journal.pone.0195959</a></dgdoi:pub-id>
    Search in Google ScholarBack to article
  30. Killworth, P., E. Johnsen, C. McCarty, G. Shelley, and H. Bernard. 1998a. “A social network approach to estimating seroprevalence in the United States.” <em>Social Networks</em> 20: 23–50. DOI: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1016/S0378-8733(96)00305-X" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/S0378-8733(96)00305-X</a>">https://doi.org/10.1016/S0378-8733(96)00305-X</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1016/S0378-8733(96)00305-X" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/S0378-8733(96)00305-X</a></dgdoi:pub-id>
    Search in Google ScholarBack to article
  31. Killworth, P., C. McCarty, H. Bernard, G. Shelley, and E. Johnsen. 1998b. “Estimation of seroprevalence, rape and homelessness in the United States using a social network approach.” <em>Evaluation Review</em> 22: 289–308. DOI: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1177/0193841X9802200205" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1177/0193841X9802200205</a>">https://doi.org/10.1177/0193841X9802200205</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1177/0193841X9802200205" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1177/0193841X9802200205</a></dgdoi:pub-id><dgpm:pub-id xmlns:dgpm="http://degruyter.com/resources/fetched-pubmed-id" pub-id-type="pmid">10183307</dgpm:pub-id>
    Search in Google ScholarBack to article
  32. Klovdahl, A.S. 1989. “Urban Social Networks: Some Methodological Problems and Possibilities.” In <em>The Small World</em>, edited by M. Kochen, 176–210. Norwood, NJ: Ablex.
    Search in Google ScholarBack to article
  33. Korn, E.L., and B.I. Graubard. 1998. “Confidence intervals for proportions with small expected number of positive counts estimated from survey data.” <em>Survey Methodology</em> 24: 193–201.
    Search in Google ScholarBack to article
  34. Lee, S., J. Wagner, R. Valliant, and S. Heeringa. 2014. “Recent developments of sampling hard-to-survey populations: An assessment.” In <em>Hard-to-Survey Populations</em>, edited by R. Tourangeau, B. Edwards, T. Johnson, K. Wolter, and N. Bates (Eds.), 424–444. Cambridge: Cambridge University Press. DOI: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1017/CBO9781139381635.025" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1017/CBO9781139381635.025</a>">https://doi.org/10.1017/CBO9781139381635.025</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1017/CBO9781139381635.025" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1017/CBO9781139381635.025</a></dgdoi:pub-id>
    Search in Google ScholarBack to article
  35. MacKellar, D., L. Valleroy, J. Karon, G. Lemp, and R. Janssen. 1996. “The Young Men’s Survey: Methods for estimating HIV seroprevalence and risk factors among young men who have sex with men.” <em>Public Health Reports</em> 111(Suppl. 1): 138–44. Available at: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1382056/pdf/pubhealthrep00044-0140.pdf">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1382056/pdf/pubhealthrep00044-0140.pdf</ext-link> (accessed April 2020).
    Search in Google ScholarBack to article
  36. Magnani, R., K. Sabin, T. Saidel, and D. Heckathorn. 2005. “Review of sampling hard-to-reach populations for HIV surveillance.” <em>AIDS</em> 19: S67–S72. DOI: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1097/01.aids.0000172879.20628.e1" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1097/01.aids.0000172879.20628.e1</a>">https://doi.org/10.1097/01.aids.0000172879.20628.e1</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1097/01.aids.0000172879.20628.e1" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1097/01.aids.0000172879.20628.e1</a></dgdoi:pub-id><dgpm:pub-id xmlns:dgpm="http://degruyter.com/resources/fetched-pubmed-id" pub-id-type="pmid">15930843</dgpm:pub-id>
    Search in Google ScholarBack to article
  37. Maltiel, R., A.E. Raftery, T.H. McCormick, and A.J. Baraff. 2015. “Estimating population size using the network scale up method.” <em>The Annals of Applied Statistics</em> 9: 1247–1277. DOI: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1214/15-AOAS827" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1214/15-AOAS827</a>">https://doi.org/10.1214/15-AOAS827</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1214/15-AOAS827" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1214/15-AOAS827</a></dgdoi:pub-id><dgpm:pub-id xmlns:dgpm="http://degruyter.com/resources/fetched-pubmed-id" pub-id-type="pmcid">4777323</dgpm:pub-id><dgpm:pub-id xmlns:dgpm="http://degruyter.com/resources/fetched-pubmed-id" pub-id-type="pmid">26949438</dgpm:pub-id>
    Search in Google ScholarBack to article
  38. Marpsat, M., and N. Razafindratsima. 2010. “Survey methods for hard-to-reach populations: Introduction to the special issue.” <em>Methodological Innovations Online</em> 5: 3–16. DOI: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.4256/mio.2010.0014" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.4256/mio.2010.0014</a>">https://doi.org/10.4256/mio.2010.0014</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.4256/mio.2010.0014" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.4256/mio.2010.0014</a></dgdoi:pub-id>
    Search in Google ScholarBack to article
  39. McCormick, T.H., M.J. Salganik, and T. Zheng. 2010. “How many people do you know? Efficiently estimating personal network size.” <em>Journal of the American Statistical Association</em> 105: 59–70. DOI: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1198/jasa.2009.ap08518" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1198/jasa.2009.ap08518</a>">https://doi.org/10.1198/jasa.2009.ap08518</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1198/jasa.2009.ap08518" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1198/jasa.2009.ap08518</a></dgdoi:pub-id><dgpm:pub-id xmlns:dgpm="http://degruyter.com/resources/fetched-pubmed-id" pub-id-type="pmcid">3666355</dgpm:pub-id><dgpm:pub-id xmlns:dgpm="http://degruyter.com/resources/fetched-pubmed-id" pub-id-type="pmid">23729943</dgpm:pub-id>
    Search in Google ScholarBack to article
  40. Meng, V.Y., and P. Gustafson. 2017. “Inferring population size: extending the multiplier method to incorporate multiple traits with a likelihood-based approach.” <em>Stat</em> 6: 4–13. DOI: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1002/sta4.131" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1002/sta4.131</a>">https://doi.org/10.1002/sta4.131</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1002/sta4.131" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1002/sta4.131</a></dgdoi:pub-id>
    Search in Google ScholarBack to article
  41. Pledger, S. 2000. “Unified maximum likelihood estimates for closed capture-recapture models using mixtures.” <em>Biometrics</em> 56: 434–442. DOI: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1111/j.0006-341X.2000.00434.x" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1111/j.0006-341X.2000.00434.x</a>">https://doi.org/10.1111/j.0006-341X.2000.00434.x</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1111/j.0006-341X.2000.00434.x" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1111/j.0006-341X.2000.00434.x</a></dgdoi:pub-id>
    Search in Google ScholarBack to article
  42. R Core Team. 2018. <em>R: A Language and Environment for Statistical Computing</em>. Vienna, Austria: R Foundation for Statistical Computing. Available at: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://www.R-project.org/">https://www.R-project.org/</ext-link> (accessed April 2020).
    Search in Google ScholarBack to article
  43. Reiser, M. 2019. “Goodness of fit testing in sparse contingency tables when the number of variables is large.” <em>Wiley Interdisciplinary Reviews: Computational Statistics</em> 11(6): e1470. DOI: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1002/wics.1470" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1002/wics.1470</a>">https://doi.org/10.1002/wics.1470</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1002/wics.1470" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1002/wics.1470</a></dgdoi:pub-id>
    Search in Google ScholarBack to article
  44. Sanathanan, L. 1972. “Estimating the size of a multinomial population.” <em>Annals of Mathematical Statistics</em> 43: 142–152. DOI: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1214/aoms/1177692709" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1214/aoms/1177692709</a>">https://doi.org/10.1214/aoms/1177692709</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1214/aoms/1177692709" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1214/aoms/1177692709</a></dgdoi:pub-id>
    Search in Google ScholarBack to article
  45. Särndal, C.-E., B. Swensson, and J. Wretman. 1992. <em>Model Assisted Survey Sampling</em>. New York: Springer-Verlag.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1007/978-1-4612-4378-6" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1007/978-1-4612-4378-6</a></dgdoi:pub-id>
    Search in Google ScholarBack to article
  46. Spreen, M. 1992. “Rare populations, hidden populations and link-tracing designs: What and why?” <em>Bulletin de Méthodologie Sociologique</em> 36: 34–58. DOI: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1177/075910639203600103" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1177/075910639203600103</a>">https://doi.org/10.1177/075910639203600103</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1177/075910639203600103" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1177/075910639203600103</a></dgdoi:pub-id>
    Search in Google ScholarBack to article
  47. Spreen, M., and S. Bogaerts. 2015. “B-Graph Sampling to Estimate the Size of a Hidden Population.” <em>Journal of Official Statistics</em> 31: 723–736. DOI: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1515/-jos-2015-0042" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1515/-jos-2015-0042</a>">https://doi.org/10.1515/-jos-2015-0042</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1515/jos-2015-0042" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1515/jos-2015-0042</a></dgdoi:pub-id>
    Search in Google ScholarBack to article
  48. Staudte, R.G., and S.J. Sheather. 1990. <em>Robust Estimation and Testing</em>. New York: Wiley.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1002/9781118165485" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1002/9781118165485</a></dgdoi:pub-id>
    Search in Google ScholarBack to article
  49. St. Clair, K., and D. O’Connell. 2012. “A Bayesian model for estimating population means using a link-tracing sampling design.” <em>Biometrics</em> 68: 165–173. DOI: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1111/j.1541-0420.2011.01631.x" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1111/j.1541-0420.2011.01631.x</a>">https://doi.org/10.1111/j.1541-0420.2011.01631.x</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1111/j.1541-0420.2011.01631.x" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1111/j.1541-0420.2011.01631.x</a></dgdoi:pub-id><dgpm:pub-id xmlns:dgpm="http://degruyter.com/resources/fetched-pubmed-id" pub-id-type="pmid">21668905</dgpm:pub-id>
    Search in Google ScholarBack to article
  50. Thompson, S.K. 2012. <em>Sampling, Third edition</em>. New Jersey: Wiley.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1002/9781118162934" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1002/9781118162934</a></dgdoi:pub-id>
    Search in Google ScholarBack to article
  51. Thompson, S.K., and O. Frank. 2000. “Model-based estimation with link-tracing sampling designs.” <em>Survey Methodology</em> 26: 87–98. Available at <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://www150.statcan.gc.ca/-n1/en/pub/12-001-x/2000001/article/5181-eng.pdf?st=dgk9U6pj">https://www150.statcan.gc.ca/-n1/en/pub/12-001-x/2000001/article/5181-eng.pdf?st=dgk9U6pj</ext-link> (accessed April 2020).
    Search in Google ScholarBack to article
  52. Tourangeau, R. 2014. “Defining hard-to-survey populations.” In <em>Hard-to-Survey Populations</em>, edited by R. Tourangeau, B. Edwards, T. Johnson, K. Wolter, and N. Bates (Eds.), 3–20. Cambridge: Cambridge University Press. DOI: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1017/CBO9781139381635.003" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1017/CBO9781139381635.003</a>">https://doi.org/10.1017/CBO9781139381635.003</ext-link>.<dgdoi:pub-id xmlns:dgdoi="http://degruyter.com/resources/doi-from-crossref" pub-id-type="doi"><a href="https://doi.org/10.1017/CBO9781139381635.003" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1017/CBO9781139381635.003</a></dgdoi:pub-id>
    Search in Google ScholarBack to article
  53. UNAIDS/WHO (World Health Organization Working Group on Global HIV/AIDS, and STI Surveillance). 2010. <em>Guidelines on estimating the size of populations most at risk to HIV</em>. UNAIDS– Joint United Nations Programme on HIV/AIDS. Available at: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://data.unaids.org/pub/manual/2010/guidelines_popnestimationsize_en.pdf">https://data.unaids.org/pub/manual/2010/guidelines_popnestimationsize_en.pdf</ext-link>. (accessed June 2015).
    Search in Google ScholarBack to article
  54. Volz, E., and D. Heckathorn. 2008. “Probability based estimation theory for respondent driven sampling.” <em>Journal of Official Statistics</em> 24: 79–97. Available at: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://www.sverigeisiffror.scb.se/contentassets/ff271eeeca694f47ae99b942de61df83/probability-based-estimation-theory-for-respondent-driven-sampling.pdf">http://www.sverigeisiffror.scb.se/contentassets/ff271eeeca694f47ae99b942de61df83/probability-based-estimation-theory-for-respondent-driven-sampling.pdf</ext-link> (accessed April 2020).
    Search in Google ScholarBack to article
  55. Williams, B.K., J.D. Nichols, and M.J. Conroy. 2002. <em>Analysis and Management of Animal Populations</em>. San Diego, California: Academic Press.
    Search in Google ScholarBack to article
DOI: https://doi.org/10.2478/jos-2021-0038 | Journal eISSN: 2001-7367
Journal RSS Feed
Language: English
Page range: 865 - 905
Submitted on: Jun 1, 2020
Accepted on: Jun 1, 2021
Published on: Dec 26, 2021
Published by: Sciendo
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year
Keywords:
Capture-recapture,
Hájek estimator,
Horvitz-Thompson estimator,
maximum likelihood estimator,
snowball sampling
Related subjects:
Mathematics,
Probability and statistics

© 2021 Martín Humberto Félix-Medina, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Previous articleVolume 37 (2021): Issue 4 (December 2021)Next article