Penalized orthogonal-components regression for large \(p\) small \(n\) data

DOI10.1214/09-EJS354zbMath1326.62149arXiv0811.4167OpenAlexW2138769430MaRDI QIDQ1952001

Yanzhu Lin, Dabao Zhang, Min Zhang

Publication date: 27 May 2013

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0811.4167

zbMATH Keywords

POCREsupervised dimension reduction\(p\gg n\) dataempirical Bayes thresholdinglatent-variable modelsparse predictors

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Measures of association (correlation, canonical correlation, etc.) (62H20) General nonlinear regression (62J02)

Related Items (3)

Generalized orthogonal components regression for high dimensional generalized linear models ⋮ Consistent High-Dimensional Bayesian Variable Selection via Penalized Credible Regions ⋮ Bayesian variable selection with shrinking and diffusing priors

Uses Software

• spls
• mixOmics
• EbayesThresh
• EBayesThresh

Cites Work

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• Fisher lecture: Dimension reduction in regression
• Sparse principal component analysis via regularized low rank matrix approximation
• Heuristics of instability and stabilization in model selection
• Needles and straw in haystacks: Empirical Bayes estimates of possibly sparse sequences
• A Sparse PLS for Variable Selection when Integrating Omics Data
• The Bayesian Lasso
• An Interpretation of Partial Least Squares
• Ideal spatial adaptation by wavelet shrinkage
• Sparsity and Smoothness Via the Fused Lasso
• Regularization and Variable Selection Via the Elastic Net
• Model Selection and Estimation in Regression with Grouped Variables
• Large-Scale Simultaneous Hypothesis Testing
• Prediction by Supervised Principal Components

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