Electronic Journal of Statistics

Unequal edge inclusion probabilities in link-tracing network sampling with implications for Respondent-Driven Sampling

Miles Q. Ott and Krista J. Gile

Full-text: Open access

Abstract

Respondent-Driven Sampling (RDS) is a widely adopted link-tracing sampling design used to draw valid statistical inference from samples of populations for which there is no available sampling frame. RDS estimators rely upon the assumption that each edge (representing a relationship between two individuals) in the underlying network has an equal probability of being sampled. We show that this assumption is violated in even the simplest cases, and that RDS estimators are sensitive to the violation of this assumption.

Article information

Source
Electron. J. Statist. Volume 10, Number 1 (2016), 1109-1132.

Dates
Received: June 2015
First available in Project Euclid: 29 April 2016

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1461947420

Digital Object Identifier
doi:10.1214/16-EJS1138

Mathematical Reviews number (MathSciNet)
MR3492037

Zentralblatt MATH identifier
1335.62029

Keywords
Respondent-driven sampling link tracing network sampling edge inclusion random walk

Citation

Ott, Miles Q.; Gile, Krista J. Unequal edge inclusion probabilities in link-tracing network sampling with implications for Respondent-Driven Sampling. Electron. J. Statist. 10 (2016), no. 1, 1109--1132. doi:10.1214/16-EJS1138. https://projecteuclid.org/euclid.ejs/1461947420.


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