What is a hot deck?

Hot deck/cold deck systems are an air handler based solution where the flow for the building is split into two, with one part being heated and one part being cooled. These two airflows are then mixed together to create the right amount of heating and cooling for each space.

What is hot deck and cold deck imputation?

Hot-deck imputation – a donor questionnaire is found from the same survey as the questionnaire with the missing item. The “nearest neighbour” search technique is often used to expedite the search for a donor record. Cold-deck imputation – same as hot deck except that the data is found in a previously conducted similar.

Does mean imputation reduce variance?

Mean imputation reduces variance The statistics for the original variable are computed by using listwise deletion, which means that missing observations are dropped from the analysis. Thus the variance of the mean-imputed variable is always smaller than the variance of the original variable.

What is the hot deck method for imputation?

Hot-deck imputation is a popular and widely used imputation method to handle missing data. The [Page 316] method involves filling in missing data on variables of interest from nonrespondents (or recipients) using observed values from respondents (i.e. donors) within the same survey data set.

How is the predicted value obtained in the imputation model?

The predicted value obtained by regressing the missing variable on other variables. So instead of just taking the mean, you’re taking the predicted value, based on other variables. This preserves relationships among variables involved in the imputation model, but not variability around predicted values.

Why are punch cards called a hot deck?

The term hot deck, in contrast with cold deck, dates back to the storage of data on punch cards. It indicates that the donors and the recipients are from the same data set; the stack of cards was “hot” because it was currently being processed (i.e. run through the card reader Looks like you do not have access to this content.

What does it mean to use imputation in Hadoop?

Another common approach among those who are paying attention is imputation. Imputation simply means replacing the missing values with an estimate, then analyzing the full data set as if the imputed values were actual observed values. How do you choose that estimate? The following are common methods: