Others, like author Fabian Pascal, have stated a belief that "how the function calculation should treat missing values is not governed by the relational model".
The decision to treat missing data as normal or abnormal did not affect model calibration.
In this study, we experimented with four methods of treating missing values in a clinical data set two of them explicitly model the absence or "missingness" of data.
Treating missing labels as negative labels is one of the approaches of handling missing labels in MLC classification [45].
We ran stratified, stage-specific models, treating patients with missing stage as a distinct category.
The evolutionary distance, K, between CBF promoters (500 bp upstream from the TU) were computed using the Kimura 2-paramater substitution model, and gaps were treated as missing data.
For the multivariate models, missing was treated as a separate category for discrete covariates and an indicator for missing was added to the model for continuous covariates.
Within the mixed-effects modeling framework, the random effects can be treated as missing data so that the EM algorithm is capable of estimating fixed effects after averaging random effects in the E-step.
