 Using data augmentation to correct for non‐ignorable non‐response when surrogate data are available: an application to the distribution of hourly payGabriele B. Durrant and Chris Skinner Journal of the Royal Statistical Society Series A, 2006, vol. 169, issue 3, 605-623 Abstract: Summary. The paper develops a data augmentation method to estimate the distribution function of a variable, which is partially observed, under a non‐ignorable missing data mechanism, and where surrogate data are available. An application to the estimation of hourly pay distributions using UK Labour Force Survey 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 method proposed is compared with standard methods that are based on an ignorable missing data mechanism, both in a simulation study and in the Labour Force Survey application. The focus is on reducing bias in point estimation, but variance estimation using multiple imputation is also considered briefly. Date: 2006 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssa:v:169:y:2006:i:3:p:605-623 Ordering information: This journal article can be ordered from Access Statistics for this article Journal of the Royal Statistical Society Series A is currently edited by A. Chevalier and L. Sharples More articles in Journal of the Royal Statistical Society Series A from Royal Statistical Society Contact information at EDIRC. |
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