You can set values to missing within your DATA step by using program statements such as this one: if age<0 then age=.; This statement sets the stored value of AGE to a numeric missing value if AGE has ...

Data Prep for Machine Learning: Missing Data. Turning his attention to the extremely time-consuming task of machine learning data preparation, Dr. James McCaffrey of Microsoft Research explains how to ...

Missing values of character variables are smaller than any printable character value. Therefore, when you sort a data set by a character variable, observations with missing (blank) values of the BY ...

This process reflects the uncertainty in each guess. Repeat these steps for the missing value in each of the other copies of the data set. Once you’ve filled in all the missing data, you can analyze ...

Missing-indicator method. The missing-indicator method was proposed for missing confounder data in etiologic research7, 8 and has since received much attention in the medical literature.3 – 6, 10, 12 ...

Table 2 Comparison of the accuracy of haplotype frequency estimations in the presence of missing values in the data set, restricted to estimations above 1/2000. Full size table. Figure 1.

The technique of multiple imputation involves creating multiple complete data sets by filling in values for the missing data. Then, each filled-in data set is analyzed as if it were a complete data ...