Nearest neighbour ratio imputation with incomplete multinomial outcome in survey sampling

Date
2022-05-10
Authors
Gao, Chenyin
Thompson, Katherine Jenny
Kim, Jae Kwang
Yang, Shu
Major Professor
Advisor
Committee Member
Journal Title
Journal ISSN
Volume Title
Publisher
Wiley and Royal Statistical Society
Altmetrics
Authors
Research Projects
Organizational Units
Statistics
Organizational Unit
Journal Issue
Series
Department
Statistics
Abstract
Nonresponse is a common problem in survey sampling. Appropriate treatment can be challenging, especially when dealing with detailed breakdowns of totals. Often, the nearest neighbour imputation method is used to handle such incomplete multinomial data. In this article, we investigate the nearest neighbour ratio imputation (NNRI) estimator, in which auxiliary variables are used to identify the closest donor and the vector of proportions from the donor is applied to the total of the recipient to implement ratio imputation. To estimate the asymptotic variance, we first treat the NNRI as a special case of predictive matching imputation and build on earlier work to linearize the imputed estimate. To account for the non-negligible sampling fractions, parametric and generalized additive models are employed to incorporate the smoothness of the imputation estimator, which results in a valid variance estimator. We apply the proposed method to estimate expenditures detail items based on empirical data from the 2018 collection of the Service Annual Survey, conducted by the United States Census Bureau. Our simulation results demonstrate the validity of our proposed estimators and also confirm that the derived variance estimators have good performance even when the sampling fraction is non-negligible.
Comments
This article is published as Gao, Chenyin, Katherine Jenny Thompson, Shu Yang, and Jae Kwang Kim. "Nearest neighbor ratio imputation with incomplete multi-nomial outcome in survey sampling." Journal of the Royal Statistical Society: Series A (2022). DOI: 10.1111/rssa.12841. Works produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted.
Description
Keywords
Citation
DOI
Collections