Data Driven Estimation of Imputation Error-A Strategy for Imputation with a Reject Option.

ABSTRACT: Missing data is a common problem in many research fields and is a challenge that always needs careful considerations. One approach is to impute the missing values, i.e., replace missing values with estimates. When imputation is applied, it is typically applied to all records with missing values indiscriminately. We note that the effects of imputation can be strongly dependent on what is missing. To help make decisions about which records should be imputed, we propose to use a machine learning approach to estimate the imputation error for each case with missing data. The method is thought to be a practical approach to help users using imputation after the informed choice to impute the missing data has been made. To do this all patterns of missing values are simulated in all complete cases,

SUBMITTER: Bak N 

PROVIDER: S-EPMC5056679 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature