Variable selection using stepdown procedures in high-dimensional linear models

DOI10.4064/am2286-6-2016zbMath1351.62143OpenAlexW2512494044MaRDI QIDQ2833627

Konrad Furmańczyk

Publication date: 18 November 2016

Published in: Applicationes Mathematicae (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.4064/am2286-6-2016

zbMATH Keywords

ridge regressionvariable selectionmultiple hypothesis testingstepdown procedurehigh-dimensional linear model

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05) Hypothesis testing in multivariate analysis (62H15) Asymptotic properties of parametric tests (62F05) Paired and multiple comparisons; multiple testing (62J15)

Uses Software

• R
• glmnet
• seq.f95

Cites Work

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• Statistical significance in high-dimensional linear models
• Estimation in high-dimensional linear models with deterministic design matrices
• A step-down multiple hypotheses testing procedure that controls the false discovery rate under independence.
• Consistent variable selection in high dimensional regression via multiple testing
• Generalizations of the familywise error rate
• A stagewise rejective multiple test procedure based on a modified Bonferroni test
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Probability for Statisticians
• Selection in parametric models via some stepdown procedures
• On some stepdown procedures with application to consistent variable selection in linear regression
• Exact and Approximate Stepdown Methods for Multiple Hypothesis Testing

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