Missing data imputation using classification and regression trees.

ABSTRACT:

Background

Missing data are common when analyzing real data. One popular solution is to impute missing data so that one complete dataset can be obtained for subsequent data analysis. In the present study, we focus on missing data imputation using classification and regression trees (CART).

Methods

We consider a new perspective on missing data in a CART imputation problem and realize the perspective through some resampling algorithms. Several existing missing data imputation methods using CART are compared through simulation studies, and we aim to investigate the methods with better imputation accuracy under various conditions. Some systematic findings are demonstrated and presented. These imputation methods are further applied to two real datasets: Hepatitis data and Credit

SUBMITTER: Chen CY 

PROVIDER: S-EPMC11232582 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature