Testing significance of features by lassoed principal components

DOI10.1214/08-AOAS182zbMath1149.62092arXiv0811.1700WikidataQ42128404 ScholiaQ42128404MaRDI QIDQ958329

Daniela M. Witten, Robert Tibshirani

Publication date: 3 December 2008

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

Full work available at URL: https://arxiv.org/abs/0811.1700

zbMATH Keywords

multiple testingfeature selectionmicroarraygene expression

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items

Feature selection for functional data, Searching for the core variables in principal components analysis, Asymptotics for high-dimensional covariance matrices and quadratic forms with applications to the trace functional and shrinkage, Seeking relevant information from a statistical model, Testing significance of features by lassoed principal components, A variable selection procedure for depth measures, lpc

Uses Software

• Eigenstrat

Cites Work

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• High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics
• Prediction by Supervised Principal Components