The size of the area of missing values depended on the distribution of the remaining values.
The resulting data set was the simulated data set S. The generation of missing values depended on a random starting value.
The former is addressed through the use of multiple imputation, which assumes that the missing covariate values are missing at random (MAR) - that the missing values depend on observed values of other variables, but not on the missing values themselves.
The missing value depends on the unobserved QoL scores and the missingness mechanism is informative.
Finally, the data are called missing not at random when the probability of a missing value depends on either unobserved predictors or the missing value itself.
A broader class of missing data is missing at random in which the probability of a missing value depends only on the available information.
The surrogate-split mechanism is, in effect, a flexible way to impute a missing value depending on where it is encountered in the tree.
The extension of a model m with a symbol α in spellModels must take into account the following: α can either be, or not be, the symbol used for missing value, depending on whether we are performing an extension without errors or performing an extension with errors, respectively.
If we assume that missing values might depend on θ, we should include θ into the imputation model.
