Missing data are an unavoidable complication in many machine learning tasks. When data
are 'missing at random'there exist a range of tools and techniques to deal with the issue …

Background Missing data are common in statistical analyses, and imputation methods
based on random forests (RF) are becoming popular for handling missing data especially in …

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 …

Objectives To compare the validity and robustness of five methods for handling missing
characteristics when using cardiovascular disease risk prediction models for individual …

Purpose To map the current practice of handling missing data in the field of training load and
injury risk and to determine how missing data in training load should be handled. Methods A …

Electronic health records (EHR) are patient-level information, eg, laboratory tests and
questionnaires, stored in electronic format. Compared to physical records, the EHR …

Missing data is a common problem in scientific research. The availability of extensive
environmental time series is usually laborious and difficult, and sometimes unexpected …

In this paper a new method of preprocessing incomplete data is introduced. The method is
based on clusterwise linear regression and it combines two well-known approaches for …

A new methodology, imputation by feature importance (IBFI), is studied that can be applied
to any machine learning method to efficiently fill in any missing or irregularly sampled data. It …

We consider the problem of variable selection in high-dimensional settings with missing
observations among the covariates. To address this relatively understudied problem, we …