MIBoost extends gradient boosting to multiple imputation by defining a single loss function that produces one set of selected variables across all imputed datasets.
Boosting for high-dimensional linear models.The An- nals of Statistics, 34(2):559–583, 2006
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ML 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
MIBoost: A gradient boosting algorithm for variable selection after multiple imputation
MIBoost extends gradient boosting to multiple imputation by defining a single loss function that produces one set of selected variables across all imputed datasets.