Regression Fractional Hot Deck Imputation

Weight Adjustments for Fractional Regression Hot Deck Imputation

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...

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...

Using the Fractional Imputation Methodology to Evaluate Variance due to hot Deck Imputation in Survey Data

Imputation methods for quantile estimation under missing at random

Imputation is frequently used to handle missing data for which multiple imputation is a popular technique. We propose a fractional hot deck imputation which produces a valid variance estimator for quantiles. In the proposed method, the imputed values are chosen from the set of respondents and are assigned with proper fractional weights that use a density function for the working model. In addit...

Hot Deck imputation for multivariate missing data

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...