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Abstract:
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In principle, hot deck imputation methods preserve means and variances, and can also preserve covariances with other variables in the allocation matrix. In practice, dimensionality problems arise quickly as predictive variables are added and allocation matrix cells become small, undermining the hot deck's theoretical advantages. Predictive-mean nearest-neighbor imputation avoids dimensionality problems, but does not preserve the variance. A combination method is described: using the predicted values from a set of triangular regressions to form hot deck matrices. Triangularity allows the inclusion of predictive variables that are themselves subject to non-response. The method enables the rapid development of allocation schemes, eliminates dimensionality problems, and aids in predictor selection. I evaluate the method's ability to preserve the covariance matrix of survey income data.
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