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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

Using data augmentation to correct for nonignorable nonresponse when surrogate data are available: an application to the distribution of hourly pay
Using data augmentation to correct for nonignorable nonresponse when surrogate data are available: an application to the distribution of hourly pay
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.
distribution function estimation, imputation, measurement error, missing data, multiple imputation, rejection sampling
0964-1998
605-623
Durrant, Gabriele B.
14fcc787-2666-46f2-a097-e4b98a210610
Skinner, Chris
dec5ef40-49ef-492a-8a1d-eb8c6315b8ce
Durrant, Gabriele B.
14fcc787-2666-46f2-a097-e4b98a210610
Skinner, Chris
dec5ef40-49ef-492a-8a1d-eb8c6315b8ce

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), 605-623. (doi:10.1111/j.1467-985X.2006.00398.x).

Record type: Article

Abstract

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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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

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Date deposited: 18 May 2006
Last modified: 15 Mar 2024 07:49

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Contributors

Author: Chris Skinner

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