Premium
Using data augmentation to correct for non‐ignorable non‐response when surrogate data are available: an application to the distribution of hourly pay
Author(s) -
Durrant Gabriele B.,
Skinner Chris
Publication year - 2006
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/j.1467-985x.2006.00398.x
Subject(s) - computer science , distribution (mathematics) , econometrics , statistics , mathematics , mathematical analysis
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.