Imputation of incomplete ordinal and nominal data by predictive mean matching.

ABSTRACT: Multivariate imputation using chained equations is a popular algorithm for imputing missing data that entails specifying multivariable models through conditional distributions. Two standard imputation methods for imputing missing continuous variables are parametric imputation using a linear model and predictive mean matching. The default methods for imputing missing categorical variables are parametric imputation using multinomial logistic regression and ordinal logistic regression for imputing nominal and ordinal categorical variables, respectively. There is a paucity of research into the relative computational burden and the quality of statistical inferences when using predictive mean matching versus parametric imputation for imputing missing non-binary categorical variables. We used sim

SUBMITTER: Austin PC 

PROVIDER: S-EPMC12669399 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature