Open Access
2017 Central limit theorems for network driven sampling
Xiao Li, Karl Rohe
Electron. J. Statist. 11(2): 4871-4895 (2017). DOI: 10.1214/17-EJS1333


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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Xiao Li. Karl Rohe. "Central limit theorems for network driven sampling." Electron. J. Statist. 11 (2) 4871 - 4895, 2017.


Received: 1 September 2015; Published: 2017
First available in Project Euclid: 7 December 2017

zbMATH: 06816637
MathSciNet: MR3733297
Digital Object Identifier: 10.1214/17-EJS1333

Primary: 60G17
Secondary: 62D05

Keywords: central limit theorem , Respondent-driven sampling , stochastic blockmodel

Vol.11 • No. 2 • 2017
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