Ultra-High Dimensional Quantile Regression for Longitudinal Data: An Application to Blood Pressure Analysis

DOI10.1080/01621459.2022.2128806zbMath1514.62371OpenAlexW4297983151MaRDI QIDQ6107193

Heng Lian, Yan Yu, Unnamed Author, Unnamed Author

Publication date: 3 July 2023

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

Full work available at URL: https://figshare.com/articles/dataset/Ultra-high_Dimensional_Quantile_Regression_for_Longitudinal_Data_an_Application_to_Blood_Pressure_Analysis/21235588

zbMATH Keywords

generalized estimating equationsvariable selectioncorrelated dataSCADGWAS

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Functional data analysis (62R10) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Cites Work

• Longitudinal data analysis using generalized linear models
• Robust model-free feature screening via quantile correlation
• Quantile regression in partially linear varying coefficient models
• A Gaussian pseudolikelihood approach for quantile regression with repeated measurements
• Nonconcave penalized likelihood with a diverging number of parameters.
• GEE analysis for longitudinal single-index quantile regression
• Calibrating nonconvex penalized regression in ultra-high dimension
• Variable selection using MM algorithms
• Penalized Generalized Estimating Equations for High-Dimensional Longitudinal Data Analysis
• Shrinkage Tuning Parameter Selection with a Diverging number of Parameters
• Akaike's Information Criterion in Generalized Estimating Equations
• Extended Bayesian information criteria for model selection with large model spaces
• Penalized Estimating Functions and Variable Selection in Semiparametric Regression Models
• An integrated identification methodology for control and identification
• Regression Quantiles
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Sure Independence Screening for Ultrahigh Dimensional Feature Space
• Quantile Regression for Analyzing Heterogeneity in Ultra-High Dimension
• Nonconcave Penalized Likelihood With NP-Dimensionality
• Standard errors and covariance matrices for smoothed rank estimators
• Smoothly Clipped Absolute Deviation on High Dimensions
• Ultra high‐dimensional semiparametric longitudinal data analysis

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