Variable selection models based on multiple imputation with an application for predicting median effective dose and maximum effect

DOI10.1080/00949655.2014.907801zbMath1457.62050OpenAlexW2098185117WikidataQ40493234 ScholiaQ40493234MaRDI QIDQ5220835

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Publication date: 27 March 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc4583148

zbMATH Keywords

multiple imputationvariable selectionelastic netpenalized least squares

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Sampling theory, sample surveys (62D05)

Related Items (3)

Integrating Multisource Block-Wise Missing Data in Model Selection ⋮ Variable Selection with Multiply-Imputed Datasets: Choosing Between Stacked and Grouped Methods ⋮ A comparison of model selection methods for prediction in the presence of multiply imputed data

Uses Software

• glmnet
• MICE
• mi
• ElemStatLearn
• MAMI

Cites Work

• Model selection and model averaging after multiple imputation
• Sparse Partial Least Squares Regression for Simultaneous Dimension Reduction and Variable Selection
• Regularization and Variable Selection Via the Elastic Net
• Sensitivity analysis after multiple imputation under missing at random: a weighting approach
• Ridge Regression: Biased Estimation for Nonorthogonal Problems
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