Multiple Imputation of Missing Values in Household Data with Structural Zeros

We present an approach for imputation of missing items in multivariate categor- ical data nested within households. The approach relies on a latent class model that (i) allows for household-level and individual-level variables, (ii) ensures that impossible household configurations have zero probability in the model, and (iii) can preserve multivariate distributions both within households and across households. We present a Gibbs sampler for estimating the model and generating imputations. We also describe strategies for improving the compu- tational efficiency of the model estimation. We illustrate the performance of the approach with data that mimic the variables collected in typical population censuses.

Duke Scholars

Author Jerome P. Reiter Statistical Science