$11\mathrm{.}91$ Evaluation of an imputation method for missing data.

When analyzing large data sets with many variables, re

searchers often encounter the problem of missing data

$($e$.$g$.,$ non$-$response$).$ Typically, an imputation method will

be used to substitute in reasonable values $($e$.$g$.,$ the mean of

the variable$)$ for the missing data. An imputation method

that uses $$nearest neighbors$$ as substitutes for the miss

ing data was evaluated in Data & Knowledge Engineering

$($Mar$.2013).$ Two quantitative assessment measures of the

imputation algorithm are normalized root mean square

error $($NRMSE$)$ and classification bias. The researchers

applied the imputation method to a sample of $3600$ data

sets with missing values and determined the NRMSE and