This document discusses techniques for handling missing data in statistical analysis and modeling. It compares different modeling approaches on three datasets - one on shoe preferences from a stated preference survey, one on diabetes risk factors, and one on homeowner characteristics. It finds that classification and regression tree (CART) and multivariate adaptive regression splines (MARS) techniques are preferred for imputing missing values when the data contains mixed variable types and interactions among variables. CART can sequentially impute missing values for each variable while preserving the multivariate structure of the data.