Sparse reduced-rank regression for multivariate varying-coefficient models

DOI10.1080/00949655.2020.1829622OpenAlexW3092709427MaRDI QIDQ5065249

Rui Li, Fode Zhang, Heng Lian, Dipankar Bandyopadhyay

Publication date: 18 March 2022

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2020.1829622

zbMATH Keywords

rank regressionvariable selectionvarying-coefficient models

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Nearly unbiased variable selection under minimax concave penalty
• The Adaptive Lasso and Its Oracle Properties
• Robust reduced-rank modeling via rank regression
• Incremental proximal methods for large scale convex optimization
• Estimation and variable selection for generalized additive partial linear models
• The sparsity and bias of the LASSO selection in high-dimensional linear regression
• Reduced-rank regression for the multivariate linear model
• Statistical estimation in varying coefficient models
• Simultaneous analysis of Lasso and Dantzig selector
• Variable selection in semiparametric regression modeling
• On uniform approximation by splines
• Local Rank Inference for Varying Coefficient Models
• Variable selection and estimation in high-dimensional varying-coefficient models
• Nonparametric smoothing estimates of time-varying coefficient models with longitudinal data
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Varying-coefficient models and basis function approximations for the analysis of repeated measurements
• Functional-Coefficient Autoregressive Models
• Feature Selection for Varying Coefficient Models With Ultrahigh-Dimensional Covariates
• Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Varying Coefficient Models
• Robust Nonparametric Statistical Methods
• Estimation and variable selection for semiparametric additive partial linear models
• A practical guide to splines.
• Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors