Inference for High-Dimensional Censored Quantile Regression

DOI10.1080/01621459.2021.1957900arXiv2107.10959OpenAlexW3193816867MaRDI QIDQ6165278

Yi Li, Qi Zheng, Zhe Fei, Hyokyoung Grace Hong

Publication date: 4 July 2023

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2107.10959

zbMATH Keywords

survival analysisstatistical inferenceconditional quantileshigh-dimensional predictorsfused-HDCQR

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

Scalable estimation and inference for censored quantile regression process

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• On asymptotically optimal confidence regions and tests for high-dimensional models
• Exact post-selection inference, with application to the Lasso
• Valid post-selection inference
• A general theory of hypothesis tests and confidence regions for sparse high dimensional models
• Principled sure independence screening for Cox models with ultra-high-dimensional covariates
• Globally adaptive quantile regression with ultra-high dimensional data
• The sparsity and bias of the LASSO selection in high-dimensional linear regression
• Sparse estimation and inference for censored median regression
• Censored regression quantiles
• High dimensional censored quantile regression
• Adaptive penalized quantile regression for high dimensional data
• Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data
• Adaptive robust variable selection
• Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
• p-Values for High-Dimensional Regression
• The geometry of logconcave functions and sampling algorithms
• Survival Analysis With Quantile Regression Models
• Regression Quantiles
• Censored Regression Quantiles
• Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
• Testing and Confidence Intervals for High Dimensional Proportional Hazards Models
• Quantile Regression for Analyzing Heterogeneity in Ultra-High Dimension
• Estimation and Accuracy After Model Selection
• High‐dimensional robust inference for Cox regression models using desparsified Lasso
• Drawing inferences for high‐dimensional linear models: A selection‐assisted partial regression and smoothing approach
• Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models
• Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models

This page was built for publication: Inference for High-Dimensional Censored Quantile Regression