HeißDeckVerfahren

HeißDeckVerfahren, often translated as "hot deck imputation," is a statistical technique used to handle missing data in datasets. It involves replacing missing values in a variable with observed values from a similar record within the same dataset. The "hot deck" refers to the pool of complete records from which replacement values are drawn.

The process typically begins by identifying records with missing values for a particular variable. These records are then matched with complete records based on a set of similarity criteria, often called matching variables. These matching variables are assumed to be correlated with the variable that has missing data. Once a match is found, the value of the variable from the complete record is used to fill in the missing value in the incomplete record. There are various strategies for selecting the "hot deck" donor, including using the most similar record, a random record from a similar group, or an average of values from similar records.

HeißDeckVerfahren is considered a relatively simple and intuitive method for imputation. It preserves the original distribution of the variable better than some other imputation methods because it uses actual observed values. However, it can introduce bias if the matching variables do not adequately capture the relationships with the variable containing missing data. It also assumes that the missing data mechanism is "missing at random" or "missing completely at random." The choice of matching variables and the selection strategy for the hot deck donor are critical to the effectiveness of this imputation method.