Variable selection and prediction with incomplete high-dimensional data

DOI10.1214/15-AOAS899zbMath1454.62028OpenAlexW2315490857WikidataQ31099596 ScholiaQ31099596MaRDI QIDQ288607

Melanie M. Wall, Yang Feng, Ying Liu, Yuanjia Wang

Publication date: 27 May 2016

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.aoas/1458909922

zbMATH Keywords

missing datamultiple imputationvariable selectionrandom Lassostability selectionvariable ranking

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Ridge regression; shrinkage estimators (Lasso) (62J07) Applications of statistics to biology and medical sciences; meta analysis (62P10) Missing data (62D10)

Related Items (8)

Integrating Multisource Block-Wise Missing Data in Model Selection ⋮ Bayesian additive regression trees in spatial data analysis with sparse observations ⋮ An ensemble learning method for variable selection: application to high-dimensional data and missing values ⋮ Variable Selection with Multiply-Imputed Datasets: Choosing Between Stacked and Grouped Methods ⋮ Adaptive Bayesian SLOPE: Model Selection With Incomplete Data ⋮ Penalized estimating equations for generalized linear models with multiple imputation ⋮ Logistic regression with missing covariates -- parameter estimation, model selection and prediction within a joint-modeling framework ⋮ Sure independence screening in the presence of missing data

Uses Software

• MICE
• ElemStatLearn

Cites Work

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• Model Selection for Generalized Estimating Equations Accommodating Dropout Missingness
• Regularization and Variable Selection Via the Elastic Net
• Random forests

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