A permutation approach for selecting the penalty parameter in penalized model selection

DOI10.1111/biom.12359zbMath1419.62171arXiv1404.2007OpenAlexW1747588674WikidataQ36470322 ScholiaQ36470322MaRDI QIDQ2809556

Jeremy A. Sabourin, Andrew B. Nobel, William Valdar

Publication date: 30 May 2016

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1404.2007

zbMATH Keywords

variable selectionpenalized regressionLasso

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12)

Related Items (6)

Bayesian penalized Buckley-James method for high dimensional bivariate censored regression models ⋮ Shrinkage parameter selection via modified cross-validation approach for ridge regression model ⋮ A Tuning-free Robust and Efficient Approach to High-dimensional Regression ⋮ Discussion of “A Tuning-Free Robust and Efficient Approach to High-Dimensional Regression” ⋮ Prediction error bounds for linear regression with the TREX ⋮ Tuning parameter calibration for \(\ell_1\)-regularized logistic regression

Uses Software

• glmnet
• selectiveInference

Cites Work

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• A significance test for the lasso
• How Correlations Influence Lasso Prediction
• Square-root lasso: pivotal recovery of sparse signals via conic programming
• Scaled sparse linear regression
• A study of error variance estimation in Lasso regression
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Linear Model Selection by Cross-Validation

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