Robust variable selection in high-dimensional varying coefficient models based on weighted composite quantile regression

DOI10.1007/s00362-015-0736-5zbMath1393.62015OpenAlexW2202644012MaRDI QIDQ1685286

Chaohui Guo, Hu Yang, Jing Lv

Publication date: 13 December 2017

Published in: Statistical Papers (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00362-015-0736-5

zbMATH Keywords

model selectionhigh dimensionalityoracle propertySCADvarying coefficient modelweighted composite quantile regression

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20) Nonparametric robustness (62G35)

Related Items (8)

Robust estimation and shrinkage in ultrahigh dimensional expectile regression with heavy tails and variance heterogeneity ⋮ Robust estimation for partial functional linear regression models based on FPCA and weighted composite quantile regression ⋮ Modified adaptive group lasso for high-dimensional varying coefficient models ⋮ Regression estimation via information-weighted composite models with different dimensions ⋮ Quantile regression of ultra-high dimensional partially linear varying-coefficient model with missing observations ⋮ Robust and efficient estimator for simultaneous model structure identification and variable selection in generalized partial linear varying coefficient models with longitudinal data ⋮ Weighted quantile regression in varying-coefficient model with longitudinal data ⋮ Robust estimation for varying coefficient partially functional linear regression models based on exponential squared loss function

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