A sequential rejection testing method for high-dimensional regression with correlated variables

DOI10.1515/ijb-2015-0008MaRDI QIDQ6632724

Bühlmann Peter, Jacopo Mandozzi

Publication date: 5 November 2024

Published in: The International Journal of Biostatistics (Search for Journal in Brave)

zbMATH Keywords

hierarchical clusteringlinear modelfamilywise error ratemultiple testingLassohigh-dimensional variable selectionsample splittinginheritance procedureminimal true detectionsequential rejection principlesingleton true detection

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• On asymptotically optimal confidence regions and tests for high-dimensional models
• Statistical significance in high-dimensional linear models
• Correlated variables in regression: clustering and sparse estimation
• Statistics for high-dimensional data. Methods, theory and applications.
• The sequential rejection principle of familywise error control
• High-dimensional variable selection
• A significance test for the lasso
• High-dimensional variable screening and bias in subsequent inference, with an empirical comparison
• Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
• p-Values for High-Dimensional Regression
• Multiple Comparisons Among Means
• Hierarchical testing of variable importance
• Modified Sequentially Rejective Multiple Test Procedures
• Group Bound: Confidence Intervals for Groups of Variables in Sparse High Dimensional Regression Without Assumptions on the Design
• Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models

This page was built for publication: A sequential rejection testing method for high-dimensional regression with correlated variables