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  • 标题:Central limit theorems for network driven sampling
  • 本地全文:下载
  • 作者:Xiao Li ; Karl Rohe
  • 期刊名称:Electronic Journal of Statistics
  • 印刷版ISSN:1935-7524
  • 出版年度:2017
  • 卷号:11
  • 期号:2
  • 页码:4871-4895
  • DOI:10.1214/17-EJS1333
  • 语种:English
  • 出版社:Institute of Mathematical Statistics
  • 摘要:Respondent-Driven Sampling is a popular technique for sampling hidden populations. This paper models Respondent-Driven Sampling as a Markov process indexed by a tree. Our main results show that the Volz-Heckathorn estimator is asymptotically normal below a critical threshold. The key technical difficulties stem from (i) the dependence between samples and (ii) the tree structure which characterizes the dependence. The theorems allow the growth rate of the tree to exceed one and suggest that this growth rate should not be too large.
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