Semi-Standard Partial Covariance Variable Selection When Irrepresentable Conditions Fail

DOI10.5705/ss.202020.0495OpenAlexW4200219031MaRDI QIDQ5041338

No author found.

Publication date: 13 October 2022

Published in: Statistica Sinica (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1709.04840

zbMATH Keywords

Lassoirrepresentable conditionpartial correlationmodel selection consistencysmoothly clipped absolute deviation

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• ScreenClean

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection
• Variable selection in high-dimensional linear models: partially faithful distributions and the PC-simple algorithm
• Nearly unbiased variable selection under minimax concave penalty
• The Adaptive Lasso and Its Oracle Properties
• Factor-Adjusted Regularized Model Selection
• Correlated variables in regression: clustering and sparse estimation
• Are discoveries spurious? Distributions of maximum spurious correlations and their applications
• Preconditioning the Lasso for sign consistency
• The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
• Asymptotic Equivalence of Regularization Methods in Thresholded Parameter Space
• Group Variable Selection with Oracle Property by Weight-Fused Adaptive Elastic Net Model for Strongly Correlated Data
• Optimality of Graphlet Screening in High Dimensional Variable Selection
• A Constrainedℓ₁Minimization Approach to Sparse Precision Matrix Estimation
• Extended Bayesian information criteria for model selection with large model spaces
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Sure Independence Screening for Ultrahigh Dimensional Feature Space
• High Dimensional Variable Selection via Tilting
• PARTIAL CORRELATION AND CONDITIONAL CORRELATION AS MEASURES OF CONDITIONAL INDEPENDENCE
• Group variable selection for data with dependent structures
• Partial Correlation Estimation by Joint Sparse Regression Models
• Nonconcave Penalized Likelihood With NP-Dimensionality
• Variable Selection via Partial Correlation
• Regularization and Variable Selection Via the Elastic Net
• Testing for the presence of significant covariates through conditional marginal regression
• Smoothly Clipped Absolute Deviation on High Dimensions
• Model Selection and Estimation in Regression with Grouped Variables
• The Mnet method for variable selection
• High Dimensional Thresholded Regression and Shrinkage Effect
• Randomized maximum-contrast selection: subagging for large-scale regression

This page was built for publication: Semi-Standard Partial Covariance Variable Selection When Irrepresentable Conditions Fail