Imputation-Based Variable Selection Method for Block-Wise Missing Data When Integrating Multiple Longitudinal Studies

Imputation-Based Variable Selection Method for Block-Wise Missing Data When Integrating Multiple Longitudinal Studies

When integrating data from multiple sources, a common challenge is block-wise missing. Most existing methods address this issue only in cross-sectional studies. In this paper, we propose a method for variable selection when combining datasets from multiple sources in longitudinal studies. To account...

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Bibliographic Details
Main Authors: Zhongzhe Ouyang, Lu Wang, Alzheimer’s Disease Neuroimaging Initiative
Format: Article
Language:English
Published: MDPI AG 2024-03-01
Series:Mathematics
Subjects:
multiple imputation
correlated data
data integration
Online Access:https://www.mdpi.com/2227-7390/12/7/951

Internet

https://www.mdpi.com/2227-7390/12/7/951

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