Variable selection techniques after multiple imputation in high-dimensional data

DOI10.1007/s10260-019-00493-7zbMath1458.62149OpenAlexW2983259936WikidataQ126847718 ScholiaQ126847718MaRDI QIDQ2220289

Christian Heumann, Faisal Maqbool Zahid, Shahla Faisal

Publication date: 22 January 2021

Published in: Statistical Methods and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10260-019-00493-7

zbMATH Keywords

high-dimensional datamultiple imputationvariable selectionLassoRubin's rules

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Ridge regression; shrinkage estimators (Lasso) (62J07) Statistical ranking and selection procedures (62F07) Missing data (62D10) Statistical aspects of big data and data science (62R07)

Related Items (1)

An ensemble learning method for variable selection: application to high-dimensional data and missing values

Uses Software

• UCI-ml
• MICE
• ElemStatLearn

Cites Work

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• Parameterization and Bayesian Modeling

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