MixMI, a mixture of Gaussian-process and linear-regression imputers with individualized mixing weights, reports lower mean absolute scaled error than six benchmarks on all four datasets tested.
Efficacy of the indirect approach for estimating structural equation models with missing data: A comparison of five methods,
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Mixture-based Multiple Imputation Model for Clinical Data with a Temporal Dimension
MixMI, a mixture of Gaussian-process and linear-regression imputers with individualized mixing weights, reports lower mean absolute scaled error than six benchmarks on all four datasets tested.