Projection-based Inference for High-dimensional Linear Models
type: Item
instance of: scholarly article
title: Projection-based Inference for High-dimensional Linear Models
author: Sangyoon Yi
author: Xianyang Zhang
publication date: 2022-03-30T00:00:00Z
Mathematics Subject Classification ID: 62-XX
zbMATH DE Number: 7500449
zbMATH Keywords: confidence interval
zbMATH Keywords: high-dimensional linear models
zbMATH Keywords: Lasso
zbMATH Keywords: quadratic programming
describes a project that uses: selectiveInference
describes a project that uses: covTest
describes a project that uses: CorrT
describes a project that uses: hdi
MaRDI profile type: Publication
full work available at URL: ss.202019.0283
OpenAlex ID: W3176634219
cites work: Controlling the false discovery rate via knockoffs
cites work: A knockoff filter for high-dimensional selective inference
cites work: Inference on Treatment Effects after Selection among High-Dimensional Controls
cites work: Valid post-selection inference
cites work: Statistics for high-dimensional data. Methods, theory and applications.
cites work: Confidence intervals for high-dimensional linear regression: minimax rates and adaptivity
cites work: Panning for Gold: ‘Model-X’ Knockoffs for High Dimensional Controlled Variable Selection
cites work: High-dimensional empirical likelihood inference
cites work: Bootstrapping Lasso Estimators
cites work: Rates of convergence of the adaptive LASSO estimators to the oracle distribution and higher order refinements by the bootstrap
cites work: High-dimensional simultaneous inference with the bootstrap
cites work: Q5251797
cites work: Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
cites work: Debiasing the Lasso: optimal sample size for Gaussian designs
cites work: Exact post-selection inference, with application to the Lasso
cites work: Asymptotic properties of Lasso+mLS and Lasso+Ridge in sparse high-dimensional linear regression
cites work: A significance test for the lasso
cites work: High-dimensional graphs and variable selection with the Lasso
cites work: p-Values for High-Dimensional Regression
cites work: Stability Selection
cites work: A unified theory of confidence regions and testing for high-dimensional estimating equations
cites work: A general theory of hypothesis tests and confidence regions for sparse high dimensional models
cites work: A study of error variance estimation in Lasso regression
cites work: On asymptotically optimal confidence regions and tests for high-dimensional models
cites work: High-dimensional variable selection
cites work: Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models
cites work: Significance testing in non-sparse high-dimensional linear models
cites work: Linear Hypothesis Testing in Dense High-Dimensional Linear Models
DOI: 10.5705/SS.202019.0283
published in: STATISTICA SINICA
description: scientific article; zbMATH DE number 7500449