Scale calibration for high-dimensional robust regression
From MaRDI portal
Publication:2074316
DOI10.1214/21-EJS1936zbMath1493.62104arXiv1811.02096OpenAlexW4206012820MaRDI QIDQ2074316
Publication date: 9 February 2022
Published in: Electronic Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1811.02096
semiparametric efficiencyhigh-dimensional inferenceLepski's methodone-step estimationrobust linear regressionadaptive scale estimation
Related Items
Robust variable selection and estimation via adaptive elastic net S-estimators for linear regression, Robust inference for high‐dimensional single index models, Renewable Huber estimation method for streaming datasets, High-dimensional robust approximated \(M\)-estimators for mean regression with asymmetric data
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- On asymptotically optimal confidence regions and tests for high-dimensional models
- Geometric median and robust estimation in Banach spaces
- Sparse inverse covariance estimation with the graphical lasso
- The \(L_1\) penalized LAD estimator for high dimensional linear regression
- Robustness in sparse high-dimensional linear models: relative efficiency and robust approximate message passing
- High breakdown-point and high efficiency robust estimates for regression
- Interpolating thin-shell and sharp large-deviation estimates for isotropic log-concave measures
- High-dimensional regression with noisy and missing data: provable guarantees with nonconvexity
- Exponential probability inequality and convergence results for the median absolute deviation and its modifications
- A journey in single steps: robust one-step \(M\)-estimation in linear regression
- Robust and sparse estimators for linear regression models
- Semiparametric efficiency bounds for high-dimensional models
- A class of robust and fully efficient regression estimators
- Challenging the empirical mean and empirical variance: a deviation study
- Sparse least trimmed squares regression for analyzing high-dimensional large data sets
- High-dimensional covariance estimation by minimizing \(\ell _{1}\)-penalized log-determinant divergence
- Debiasing the Lasso: optimal sample size for Gaussian designs
- Sub-Gaussian estimators of the mean of a random matrix with heavy-tailed entries
- Confidence intervals for high-dimensional linear regression: minimax rates and adaptivity
- Statistical consistency and asymptotic normality for high-dimensional robust \(M\)-estimators
- Robust machine learning by median-of-means: theory and practice
- The likelihood ratio test in high-dimensional logistic regression is asymptotically a rescaled Chi-square
- Robust elastic net estimators for variable selection and identification of proteomic biomarkers
- On the asymptotic variance of the debiased Lasso
- Influence functions for penalized M-estimators
- Regularization, sparse recovery, and median-of-means tournaments
- Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors
- Adaptive robust variable selection
- Thin-shell concentration for convex measures
- Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
- A Practical Scheme and Fast Algorithm to Tune the Lasso With Optimality Guarantees
- χ 2-Confidence Sets in High-Dimensional Regression
- Square-root lasso: pivotal recovery of sparse signals via conic programming
- Least Median of Squares Regression
- Semiparametric efficiency bounds
- Model selection and estimation in the Gaussian graphical model
- Robust Linear Model Selection Based on Least Angle Regression
- Minimax Aspects of Bounded-Influence Regression
- High Breakdown-Point Estimates of Regression by Means of the Minimization of an Efficient Scale
- Asymptotics for one-step m-estimators in regression with application to combining efficiency and high breakdown point
- Estimation in Linear Regression Models with Disparate Data Points
- Efficient Bounded-Influence Regression Estimation
- On One-Step GM Estimates and Stability of Inferences in Linear Regression
- Adaptive Robust Procedures: A Partial Review and Some Suggestions for Future Applications and Theory
- One-Step Huber Estimates in the Linear Model
- Penalized Composite Quasi-Likelihood for Ultrahigh Dimensional Variable Selection
- High-Dimensional Probability
- On a Problem of Adaptive Estimation in Gaussian White Noise
- Quantile Regression for Analyzing Heterogeneity in Ultra-High Dimension
- Confidence intervals for high-dimensional Cox models
- De-Biased Sparse PCA: Inference for Eigenstructure of Large Covariance Matrices
- Rejoinder to “A Tuning-Free Robust and Efficient Approach to High-Dimensional Regression”
- Efficient Algorithms and Lower Bounds for Robust Linear Regression
- Uniform post-selection inference for least absolute deviation regression and other Z-estimation problems
- Bandits With Heavy Tail
- An alternative point of view on Lepski's method
- Estimation of High Dimensional Mean Regression in the Absence of Symmetry and Light Tail Assumptions
- Robust Statistics
- Robust Estimation of a Location Parameter
- Some Flexible Estimates of Location
- Robust Statistics
- Robust linear regression: optimal rates in polynomial time