Accuracy assessment for high-dimensional linear regression
From MaRDI portal
Publication:2413610
DOI10.1214/17-AOS1604zbMath1403.62131arXiv1603.03474OpenAlexW2963441330MaRDI QIDQ2413610
Publication date: 14 September 2018
Published in: The Annals of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1603.03474
adaptivityconfidence intervalminimaxityminimax lower boundsparsityhigh-dimensional linear regressionloss estimationaccuracy assessment
Linear regression; mixed models (62J05) Image analysis in multivariate analysis (62H35) Minimax procedures in statistical decision theory (62C20) Nonparametric tolerance and confidence regions (62G15)
Related Items
Mathematical foundations of machine learning. Abstracts from the workshop held March 21--27, 2021 (hybrid meeting), Testability of high-dimensional linear models with nonsparse structures, Sparse high-dimensional linear regression. Estimating squared error and a phase transition, Honest Confidence Sets for High-Dimensional Regression by Projection and Shrinkage, Statistical Inference for High-Dimensional Generalized Linear Models With Binary Outcomes, Adaptive confidence sets for matrix completion, Moderate-Dimensional Inferences on Quadratic Functionals in Ordinary Least Squares, Asymptotic risk and phase transition of \(l_1\)-penalized robust estimator, Adaptive estimation of the sparsity in the Gaussian vector model, Unnamed Item, Debiasing the Lasso: optimal sample size for Gaussian designs, Optimal sparsity testing in linear regression model, The distribution of the Lasso: uniform control over sparse balls and adaptive parameter tuning, Optimal Estimation of Genetic Relatedness in High-Dimensional Linear Models, Comments on: ``High-dimensional simultaneous inference with the bootstrap, Linear Hypothesis Testing in Dense High-Dimensional Linear Models
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- On asymptotically optimal confidence regions and tests for high-dimensional models
- Statistics for high-dimensional data. Methods, theory and applications.
- On adaptive inference and confidence bands
- Global testing under sparse alternatives: ANOVA, multiple comparisons and the higher criticism
- A data-driven block thresholding approach to wavelet estimation
- From Stein's unbiased risk estimates to the method of generalized cross- validation
- Estimation of the mean of a multivariate normal distribution
- An adaptation theory for nonparametric confidence intervals
- Minimax risks for sparse regressions: ultra-high dimensional phenomenons
- Detection boundary in sparse regression
- Confidence intervals for high-dimensional linear regression: minimax rates and adaptivity
- SURE-tuned tapering estimation of large covariance matrices
- Simultaneous analysis of Lasso and Dantzig selector
- Confidence sets in sparse regression
- The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
- Adaptive confidence balls
- Adaptive nonparametric confidence sets
- Square-root lasso: pivotal recovery of sparse signals via conic programming
- Adapting to Unknown Smoothness via Wavelet Shrinkage
- Scaled sparse linear regression
- Adaptive Confidence Bands for Nonparametric Regression Functions
- The LASSO Risk for Gaussian Matrices
- The Noise-Sensitivity Phase Transition in Compressed Sensing
- Minimax Rates of Estimation for High-Dimensional Linear Regression Over $\ell_q$-Balls
- EigenPrism: Inference for High Dimensional Signal-to-Noise Ratios
