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Using data augmentation to correct for nonignorable nonresponse when surrogate data are available: an application to the distribution of hourly pay

Record type: Article

This paper develops a data augmentation method to estimate the distribution function of a variable, which is partially observed, under a nonignorable missing data mechanism, and where surrogate data are available. An application to the estimation of hourly pay distributions using UK Labour Force Survey (LFS) data provides the main motivation.
In addition to considering a standard parametric data augmentation method, we consider the use of hot deck imputation methods as part of the data augmentation procedure to improve the robustness of the method. The proposed method is compared with standard methods based upon an ignorable missing data mechanism, both in a simulation study and in the LFS application. The focus is on reducing bias in point estimation, but variance estimation using multiple imputation is also considered briefly.

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Citation

Durrant, Gabriele B. and Skinner, Chris (2006) Using data augmentation to correct for nonignorable nonresponse when surrogate data are available: an application to the distribution of hourly pay Journal of the Royal Statistical Society: Series A (Statistics in Society), 169, (3), pp. 605-623. (doi:10.1111/j.1467-985X.2006.00398.x).

More information

Published date: 2006
Keywords: distribution function estimation, imputation, measurement error, missing data, multiple imputation, rejection sampling

Identifiers

Local EPrints ID: 34799
URI: http://eprints.soton.ac.uk/id/eprint/34799
ISSN: 0964-1998
PURE UUID: 15db11dd-68a3-4f2e-8e91-4796a0d6a6ab

Catalogue record

Date deposited: 18 May 2006
Last modified: 17 Jul 2017 15:49

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