Survey Methodology
Multiple imputation of missing values in household data with structural zeros

by Olanrewaju Akande, Jerome Reiter and Andrés F. BarrientosNote 1

  • Release date: June 27, 2019
More information PDF version

Abstract

We present an approach for imputation of missing items in multivariate categorical data nested within households. The approach relies on a latent class model that (i) allows for household-level and individual-level variables, (ii) ensures that impossible household configurations have zero probability in the model, and (iii) can preserve multivariate distributions both within households and across households. We present a Gibbs sampler for estimating the model and generating imputations. We also describe strategies for improving the computational efficiency of the model estimation. We illustrate the performance of the approach with data that mimic the variables collected in typical population censuses.

Key Words:      Categorical; Census; Edit; Latent; Mixture; Nonresponse.

Table of contents

How to cite

Akande, O., Reiter, J. and Barrientos, A.F. (2019). Multiple imputation of missing values in household data with structural zeros. Survey Methodology, Statistics Canada, Catalogue No. 12-001-X, Vol. 45, No. 2. Paper available at https://www150.statcan.gc.ca/n1/pub/12-001-x/2019002/article/00005-eng.htm.

Note

More information

ISSN : 1492-0921

Editorial policy

Survey Methodology publishes articles dealing with various aspects of statistical development relevant to a statistical agency, such as design issues in the context of practical constraints, use of different data sources and collection techniques, total survey error, survey evaluation, research in survey methodology, time series analysis, seasonal adjustment, demographic studies, data integration, estimation and data analysis methods, and general survey systems development. The emphasis is placed on the development and evaluation of specific methodologies as applied to data collection or the data themselves. All papers will be refereed. However, the authors retain full responsibility for the contents of their papers and opinions expressed are not necessarily those of the Editorial Board or of Statistics Canada.

Submission of Manuscripts

Survey Methodology is published twice a year in electronic format. Authors are invited to submit their articles in English or French in electronic form, preferably in Word to the Editor, (statcan.smj-rte.statcan@canada.ca, Statistics Canada, 150 Tunney’s Pasture Driveway, Ottawa, Ontario, Canada, K1A 0T6). For formatting instructions, please see the guidelines provided in the journal and on the web site (www.statcan.gc.ca/SurveyMethodology).

Note of appreciation

Canada owes the success of its statistical system to a long-standing partnership between Statistics Canada, the citizens of Canada, its businesses, governments and other institutions. Accurate and timely statistical information could not be produced without their continued co-operation and goodwill.

Standards of service to the public

Statistics Canada is committed to serving its clients in a prompt, reliable and courteous manner. To this end, the Agency has developed standards of service which its employees observe in serving its clients.

Copyright

Published by authority of the Minister responsible for Statistics Canada.

© Her Majesty the Queen in Right of Canada as represented by the Minister of Industry, 2019

Use of this publication is governed by the Statistics Canada Open Licence Agreement.

Catalogue No. 12-001-X

Frequency: Semi-annual

Ottawa

close


Table of contents

Abstract - Article main page
Section 1. Introduction
Section 2. Review of the NDPMPM model
Section 3. Handling missing data using the NDPMPM
Section 4. Strategies for speeding up the MCMC sampler
Section 5. Empirical study
Section 6. Discussion
Acknowledgements
Appendix
References

close
Date modified: