Bayesian multiple imputation for large-scale categorical data with structural zeros
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Abstract
We propose an approach for multiple imputation of items missing at random in
large-scale surveys with exclusively categorical variables that have structural zeros.
Our approach is to use mixtures of multinomial distributions as imputation engines,
accounting for structural zeros by conceiving of the observed data as a truncated sample
from a hypothetical population without structural zeros. This approach has several
appealing features: imputations are generated from coherent, Bayesian joint models
that automatically capture complex dependencies and readily scale to large numbers
of variables. We outline a Gibbs sampling algorithm for implementing the approach,
and we illustrate its potential with a repeated sampling study using public use census
microdata from the state of New York, USA.
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National Science Foundation
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2013-12-18
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Survey Methodology
Keywords
Latent class; Log-linear; Missing; Mixture; Multinomial; Nonresponse
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