Campus Units
Statistics
Document Type
Article
Publication Version
Submitted Manuscript
Publication Date
1-1-2019
Journal or Book Title
arxiv
Abstract
This paper presents theoretical results on combining non-probability and probability survey samples through mass imputation, an approach originally proposed by Rivers (2007) as sample matching without rigorous theoretical justification. Under suitable regularity conditions, we establish the consistency of the mass imputation estimator and derive its asymptotic variance formula. Variance estimators are developed using either linearization or bootstrap. Finite sample performances of the mass imputation estimator are investigated through simulation studies and an application to analyzing a non-probability sample collected by the Pew Research Centre.
Copyright Owner
The Authors
Copyright Date
2019
Language
en
File Format
application/pdf
Recommended Citation
Kim, Jae Kwang; Park, Seho; Chen, Yilin; and Wu, Changbao, "Combining Non-probability and Probability Survey Samples Through Mass Imputation" (2019). Statistics Publications. 266.
https://lib.dr.iastate.edu/stat_las_pubs/266
Included in
Design of Experiments and Sample Surveys Commons, Probability Commons, Statistical Methodology Commons
Comments
This pre-print is made available through arxiv: https://arxiv.org/abs/1812.10694v2.