# Combining Non-probability and Probability Survey Samples Through Mass Imputation

arXiv: https://arxiv.org/abs/1812.10694 · HTML: https://mlsanity.com/paper/1812.10694/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Dec 2018.

## Built on by (most cited first, 2 of 2)

- [A Review of Generalizability and Transportability](https://mlsanity.com/paper/2102.11904/) (2021)
- [Deep Neural Networks for Doubly Robust Estimation with Nonprobability Survey Samples](https://mlsanity.com/paper/2605.28762/) (2026)

## Abstract (arXiv)

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.