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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

Beissel-Durrant, Gabriele and Skinner, Chris (2004) Using data augmentation to correct for nonignorable nonresponse when surrogate data are available: An application to the distribution of hourly pay , Southampton, UK Southampton Statistical Sciences Research Institute 30pp. (S3RI Methodology Working Papers, M04/10).

Record type: Monograph (Working Paper)

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

Submitted date: 20 September 2004
Published date: 20 September 2004
Keywords: distribution function estimation, imputation, measurement error, missing data, multiple imputation, rejection sampling

Identifiers

Local EPrints ID: 9189
URI: http://eprints.soton.ac.uk/id/eprint/9189
PURE UUID: 818b4300-5b80-4162-8c0e-fa5e5188f8e1

Catalogue record

Date deposited: 28 Jun 2006
Last modified: 17 Jul 2017 17:11

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Contributors

Author: Gabriele Beissel-Durrant
Author: Chris Skinner

University divisions

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