Using the Fractional Imputation Methodology to Evaluate Variance due to hot Deck Imputation in Survey Data
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Fractional hot deck imputation
To compensate for item nonresponse, hot deck imputation procedures replace missing values with values that occur in the sample. Fractional hot deck imputation replaces each missing observation with a set of imputed values and assigns a weight to each imputed value. Under the model in which observations in an imputation cell are independently and identically distributed, fractional hot deck impu...
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Introduction: The aim of this study was to impute missing data and to compare the effect of different doses of vitamin D supplementation on insulin resistance during pregnancy. Methods: A clinical trial study was done on 104 women with diabetes and gestational age less than 12 weeks between 1391 and...
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Imputation using a regression model is a method to preserve the correlation among variables and to provide imputed point estimators. We discuss the implementation of regression imputation using fractional imputation. By a suitable choice of fractional weights, the fractional regression imputation can take the form of hot deck fractional imputation, thus no artificial values are constructed afte...
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Fractional hot deck imputation, considered in Fuller and Kim (2005), is extended to multivariate missing data. The joint distribution of the study items is nonparametrically estimated using a discrete approximation, where the discrete transformation also serves to define imputation cells. The procedure first estimates the probabilities for the cells and then imputes real observations for missin...
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Fractional regression hot deck imputation (FRHDI), suggested by J. K. Kim, imputes multiple values for each instance of a missing dependent variable. The imputed values are equal to the predicted value based on the fully observed cases plus multiple random residuals chosen from the set of empirical residuals. Fractional weights are chosen to enable variance estimation and to preserve the correl...
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