Estimation of Non-Linear Parameters with Data Collected Using Respondent-Driven Sampling

Ismael Sánchez-Borrego, María del Mar Rueda and Héctor Mullo
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Ismael Sánchez-Borrego: Department of Statistics and Operations Research, University of Granada, 18071 Granada, Spain
María del Mar Rueda: Department of Statistics and Operations Research, University of Granada, 18071 Granada, Spain
Héctor Mullo: Facultad de Ciencias, Escuela Superior Politécnica de Chimborazo (ESPOCH), 060155 Riobamba, Ecuador

Mathematics, 2020, vol. 8, issue 8, 1-10

Abstract: Respondent-driven sampling (RDS) is a snowball-type sampling method used to survey hidden populations, that is, those that lack a sampling frame. In this work, we consider the problem of regression modeling and association for continuous RDS data. We propose a new sample weight method for estimating non-linear parameters such as the covariance and the correlation coefficient. We also estimate the variances of the proposed estimators. As an illustration, we performed a simulation study and an application to an ethnic example. The proposed estimators are consistent and asymptotically unbiased. We discuss the applicability of the method as well as future research.

Keywords: Respondent-driven sampling; regression; network dependence (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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