Missing value imputation (MVI) has been studied for several decades being the basic
solution method for incomplete dataset problems, specifically those where some data …

Multiple imputation is a straightforward method for handling missing data in a principled
fashion. This paper presents an overview of multiple imputation, including important …

Abstract Machine learning with missing data has been approached in many different ways,
including feature imputation where missing feature values are estimated based on observed …

Missing data is a common problem in real-world settings and for this reason has attracted
significant attention in the statistical literature. We propose a flexible framework based on …

Missing data pose challenges to real-life data analysis. Simple ad-hoc fixes, like deletion or
mean imputation, only work under highly restrictive conditions, which are often not met in …

Multivariate imputation by chained equations (MICE) is commonly used for imputing missing
data in epidemiologic research. The “true” imputation model may contain nonlinearities …

Recently, numerous studies have been conducted on Missing Value Imputation (MVI),
intending the primary solution scheme for the datasets containing one or more missing …

Standard approaches to implement multiple imputation do not automatically incorporate
nonlinear relations like interaction effects. This leads to biased parameter estimates when …

The aim of this book is to give the reader a detailed introduction to the different approaches
to generating multiply imputed synthetic datasets. It describes all approaches that have been …

Objective To evaluate the duration of prescriptions for antibiotic treatment for common
infections in English primary care and to compare this with guideline recommendations …