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.
Robust subspace tracking with missing data and outliers: Novel algorithm with convergence guarantee,
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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.