A stochastic-approximation EM algorithm with Metropolis-Hastings imputation is proposed for logistic regression with missing mixed-type covariates and is shown to beat MICE, MissForest, mean/mode, and complete-case baselines in most tested settings.
What do we do with missing data? some o ptions for analysis of incomplete data,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ME 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Maximum Likelihood for Logistic Regression Model with Incomplete and Hybrid-Type Covariates
A stochastic-approximation EM algorithm with Metropolis-Hastings imputation is proposed for logistic regression with missing mixed-type covariates and is shown to beat MICE, MissForest, mean/mode, and complete-case baselines in most tested settings.