Thresholding least-squares inference in high-dimensional regression models

DOI10.1214/16-EJS1160zbMath1347.62131MaRDI QIDQ309566

Mihai C. Giurcanu

Publication date: 7 September 2016

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

Full work available at URL: https://projecteuclid.org/euclid.ejs/1468849973

zbMATH Keywords

F-testregression modelshigh-dimensional inferenceresidual-bootstrapthresholding least-squares

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Linear regression; mixed models (62J05)

Related Items (4)

Thresholding tests based on affine Lasso to achieve non-asymptotic nominal level and high power under sparse and dense alternatives in high dimension ⋮ Oracle GMM estimation for misspecified models via thresholding ⋮ Subsampling based variable selection for generalized linear models ⋮ Bootstrapping Lasso-type estimators in regression models

Uses Software

• glmnet
• bootlib

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• The Adaptive Lasso and Its Oracle Properties
• Rates of convergence of the adaptive LASSO estimators to the oracle distribution and higher order refinements by the bootstrap
• On the residual empirical process based on the ALASSO in high dimensions and its functional oracle property
• High-dimensional variable selection
• Covariance regularization by thresholding
• Operator norm consistent estimation of large-dimensional sparse covariance matrices
• Asymptotic behavior of M-estimators of p regression parameters when \(p^ 2/n\) is large. I. Consistency
• Asymptotic behavior of M estimators of p regression parameters when \(p^ 2/n\) is large. II: Normal approximation
• Asymptotics with increasing dimension for robust regression with applications to the bootstrap
• Bootstrapping regression models
• Bootstrap methods: another look at the jackknife
• Density estimation by wavelet thresholding
• Robust regression: Asymptotics, conjectures and Monte Carlo
• Asymptotics for Lasso-type estimators.
• The adaptive and the thresholded Lasso for potentially misspecified models (and a lower bound for the Lasso)
• Bootstrap and wild bootstrap for high dimensional linear models
• Asymptotic properties of bridge estimators in sparse high-dimensional regression models
• Boosting for high-dimensional linear models
• Bootstrapping Lasso Estimators
• Linear Model Methodology
• Asymptotic Statistics
• Ideal spatial adaptation by wavelet shrinkage
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• A Statistical View of Some Chemometrics Regression Tools
• Real Analysis and Probability
• Safe Feature Elimination in Sparse Supervised Learning
• Measure Theory and Probability Theory
• Gene expression analysis with the parametric bootstrap
• The bootstrap and Edgeworth expansion

This page was built for publication: Thresholding least-squares inference in high-dimensional regression models