High-dimensional regression with potential prior information on variable importance

DOI10.1007/s11222-022-10110-5zbMath1490.62025arXiv2109.11281OpenAlexW3201499180MaRDI QIDQ2152561

Benjamin G. Stokell, Rajen D. Shah

Publication date: 8 July 2022

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2109.11281

zbMATH Keywords

ridge regressionhigh-dimensional datamissing dataLassocorrupted datalow variance filter

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)

Related Items (1)

OrderRegression

Uses Software

• R
• glmnet
• GitHub

Cites Work

• Unnamed Item
• Unnamed Item
• Nearly unbiased variable selection under minimax concave penalty
• The Adaptive Lasso and Its Oracle Properties
• Autoregressive process modeling via the Lasso procedure
• CoCoLasso for high-dimensional error-in-variables regression
• High-dimensional regression with noisy and missing data: provable guarantees with nonconvexity
• On cross-validated Lasso in high dimensions
• Size-biased permutation of a finite sequence with independent and identically distributed terms
• Relaxed Lasso
• High-dimensional inference: confidence intervals, \(p\)-values and R-software \texttt{hdi}
• On the conditions used to prove oracle results for the Lasso
• Sparse Matrix Inversion with Scaled Lasso
• Square-root lasso: pivotal recovery of sparse signals via conic programming
• Scaled sparse linear regression
• Sparse Partial Least Squares Regression for Simultaneous Dimension Reduction and Variable Selection
• Right Singular Vector Projection Graphs: Fast High Dimensional Covariance Matrix Estimation under Latent Confounding
• The restricted consistency property of leave-nv-out cross-validation for high-dimensional variable selection
• Model Selection and Estimation in Regression with Grouped Variables
• Improved matrix uncertainty selector
• Ridge Regression: Biased Estimation for Nonorthogonal Problems

This page was built for publication: High-dimensional regression with potential prior information on variable importance