Section: New Results

Dealing with Missing Data Through Mixture Models

Participants : Christophe Biernacki, Vincent Vandewalle.

Many data sets have missing values, however the majority of statistical methods need a complete dataset to work. Thus, practitioners often use imputation or multiple imputations to complete the data as a pre-processing step. Mixture models can be used to naturally deal with missing data in an integrated way depending on the purpose. Especially, they can be used to classify the data or derive estimates for the distances. This work as been presented in an international conference [21].