Double bias correction for high-dimensional sparse additive hazards regression with covariate measurement errors

DOI10.1007/s10985-022-09568-2OpenAlexW4286587834MaRDI QIDQ6099545

Jiayu Huang, Guosheng Yin, Unnamed Author, Xiaobo Wang, Yuan Shan Wu

Publication date: 20 June 2023

Published in: Lifetime Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10985-022-09568-2

zbMATH Keywords

survival analysisconfidence intervalestimating equationbias correctionhigh dimensionserror-in-variable

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Cites Work

• Group Inference in High Dimensions with Applications to Hierarchical Testing
• Covariate Selection in High-Dimensional Generalized Linear Models With Measurement Error
• On asymptotically optimal confidence regions and tests for high-dimensional models
• Exact post-selection inference, with application to the Lasso
• The benefit of group sparsity in group inference with de-biased scaled group Lasso
• Oracle inequalities for the lasso in the Cox model
• Minimax risk of matrix denoising by singular value thresholding
• A general theory of hypothesis tests and confidence regions for sparse high dimensional models
• Statistics for high-dimensional data. Methods, theory and applications.
• Sparse recovery under matrix uncertainty
• CoCoLasso for high-dimensional error-in-variables regression
• High-dimensional regression with noisy and missing data: provable guarantees with nonconvexity
• High-dimensional variable selection
• A significance test for the lasso
• Analysis of error-prone survival data under additive hazards models: measurement error effects and adjustments
• The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
• Corrected-loss estimation for quantile regression with covariate measurement errors
• Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
• p-Values for High-Dimensional Regression
• Unbiased estimation of a nonlinear function a normal mean with application to measurement err oorf models
• Penalized Estimating Functions and Variable Selection in Semiparametric Regression Models
• Semiparametric analysis of the additive risk model
• The Asymptotic Variance of Semiparametric Estimators
• Additive Hazards Regression with Covariate Measurement Error
• Cox Regression with Accurate Covariates Unascertainable: A Nonparametric-Correction Approach
• Testing and Confidence Intervals for High Dimensional Proportional Hazards Models
• High-Dimensional Statistics
• High-Dimensional Sparse Additive Hazards Regression
• Confidence intervals for high-dimensional Cox models
• Double/debiased machine learning for treatment and structural parameters
• Measurement Error in Nonlinear Models
• Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Double bias correction for high-dimensional sparse additive hazards regression with covariate measurement errors