Adaptively weighted group Lasso for semiparametric quantile regression models

DOI10.3150/18-BEJ1091zbMath1429.62140MaRDI QIDQ2325373

Toshio Honda, Wei-Ying Wu, Ching-Kang Ing

Publication date: 25 September 2019

Published in: Bernoulli (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.bj/1569398767

zbMATH Keywords

varying coefficient modelsadditive modelsquantile regressionB-splineLassostructure identificationhigh-dimensional information criteria

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Ridge regression; shrinkage estimators (Lasso) (62J07) Asymptotic properties of nonparametric inference (62G20)

Related Items (7)

The information detection for the generalized additive model ⋮ Forward variable selection for sparse ultra-high-dimensional generalized varying coefficient models ⋮ QuantRegGLasso ⋮ Forward variable selection for ultra-high dimensional quantile regression models ⋮ Forward selection for feature screening and structure identification in varying coefficient models ⋮ Penalized kernel quantile regression for varying coefficient models ⋮ Adaptively weighted group Lasso for semiparametric quantile regression models

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Sure independence screening in generalized linear models with NP-dimensionality
• The Adaptive Lasso and Its Oracle Properties
• A stepwise regression method and consistent model selection for high-dimensional sparse linear models
• Local linear smoothing for sparse high dimensional varying coefficient models
• Inference for single-index quantile regression models with profile optimization
• Variable selection in high-dimensional quantile varying coefficient models
• Nonparametric independence screening and structure identification for ultra-high dimensional longitudinal data
• Consistent model selection criteria for quadratically supported risks
• Statistics for high-dimensional data. Methods, theory and applications.
• Semiparametric quantile regression estimation in dynamic models with partially varying coefficients
• Globally adaptive quantile regression with ultra-high dimensional data
• Quantile regression in partially linear varying coefficient models
• Oracle inequalities for sparse additive quantile regression in reproducing kernel Hilbert space
• Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data
• Adaptively weighted group Lasso for semiparametric quantile regression models
• Simultaneous analysis of Lasso and Dantzig selector
• Variable selection and structure identification for varying coefficient Cox models
• \(\ell_1\)-penalized quantile regression in high-dimensional sparse models
• Adaptive robust variable selection
• Strong oracle optimality of folded concave penalized estimation
• Model Selection for Cox Models with Time-Varying Coefficients
• Forward Regression for Ultra-High Dimensional Variable Screening
• Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Additive Models
• Linear or Nonlinear? Automatic Structure Discovery for Partially Linear Models
• Extended Bayesian information criteria for model selection with large model spaces
• Can Tests for Jumps be Viewed as Tests for Clusters?
• The Group Lasso for Logistic Regression
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Model Selection via Bayesian Information Criterion for Quantile Regression Models
• Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Varying Coefficient Models
• Model Selection and Estimation in Regression with Grouped Variables
• Partially linear additive quantile regression in ultra-high dimension

This page was built for publication: Adaptively weighted group Lasso for semiparametric quantile regression models