Variance estimation with Hot Deck imputation using a model - ARCHIVED

When imputation is used to assign values for missing items in sample surveys, naïve methods of estimating the variances of survey estimates that treat the imputed values as if they were observed give biased variance estimates. This article addresses the problem of variance estimation for a linear estimator in which missing values are assigned by a single hot deck imputation (a form of imputation that is widely used in practice). We propose estimators of the variance of a linear hot deck imputed estimator using a decomposition of the total variance suggested by Särndal (1992). A conditional approach to variance estimation is developed that is applicable to both weighted and unweighted hot deck imputation. Estimation of the variance of a domain estimator is also examined.