Predictive mean matching

Predictive Mean Matching (PMM) is a statistical technique used in the context of handling missing data, particularly within the framework of multiple imputation. The main goal of PMM is to generate plausible values for missing data based on observed data, while preserving the distributional characteristics of the original dataset. ### Key Features of Predictive Mean Matching: 1. **Model-Based Approach**: PMM begins by fitting a regression model to predict the variable with missing values using other observed variables in the dataset.