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Contribution to Book
Nearest Neighbor Imputation for General Parameter Estimation in Survey Sampling
The Econometrics of Complex Survey Data: Theory and Applications
  • Shu Yang, North Carolina State University
  • Jae Kwang Kim, Iowa State University
Document Type
Book Chapter
Publication Version
Submitted Manuscript
Publication Date
1-1-2019
DOI
10.1108/S0731-905320190000039012
Abstract

Nearest neighbor imputation has a long tradition for handling item nonresponse in survey sampling. In this article, we study the asymptotic properties of the nearest neighbor imputation estimator for general population parameters, including population means, proportions and quantiles. For variance estimation, we propose novel replication variance estimation, which is asymptotically valid and straightforward to implement. The main idea is to construct replicates of the estimator directly based on its asymptotically linear terms, instead of individual records of variables. The simulation results show that nearest neighbor imputation and the proposed variance estimation provide valid inferences for general population parameters.

Comments

This is a manuscript of a chapter from Yang, S. and Kim, J. (2019), "Nearest Neighbor Imputation for General Parameter Estimation in Survey Sampling", The Econometrics of Complex Survey Data (Advances in Econometrics, Vol. 39), Emerald Publishing Limited, pp. 209-234. doi: 10.1108/S0731-905320190000039012. Posted with permission.

Copyright Owner
Emerald Publishing Limited
Language
en
File Format
application/pdf
Citation Information
Shu Yang and Jae Kwang Kim. "Nearest Neighbor Imputation for General Parameter Estimation in Survey Sampling" The Econometrics of Complex Survey Data: Theory and Applications Vol. 39 (2019) p. 209 - 234
Available at: http://works.bepress.com/jae-kwang-kim/59/