Robust estimation for partially linear models with large-dimensional covariates

DOI10.1007/s11425-013-4675-0OpenAlexW83569299WikidataQ33767871 ScholiaQ33767871MaRDI QIDQ2441137

Li-ping Zhu, Run-Ze Li, Heng-Jian Cui

Publication date: 21 March 2014

Published in: Science China. Mathematics (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc4060755

zbMATH Keywords

semiparametric modelsrobust model selectionpartially linear modelssmoothly clipped absolute deviation (SCAD)

Mathematics Subject Classification ID

Robustness and adaptive procedures (parametric inference) (62F35)

Related Items

WLAD-LASSO method for robust estimation and variable selection in partially linear models, M-estimation and model identification based on double SCAD penalization, Restricted profile estimation for partially linear models with large-dimensional covariates, Partially linear estimation using sufficient dimension reduction, Partially Linear Expectile Regression Using Local Polynomial Fitting, A robust and efficient estimation and variable selection method for partially linear models with large-dimensional covariates, Partial functional linear quantile regression, Variable selection for partially linear models via Bayesian subset modeling with diffusing prior, Corrected-loss estimation for error-in-variable partially linear model, Robust group non-convex estimations for high-dimensional partially linear models

Cites Work

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• Composite quantile regression and the oracle model selection theory
• \(M\)-estimation of linear models with dependent errors
• SCAD-penalized regression in high-dimensional partially linear models
• Convergence rates for parametric components in a partly linear model
• Asymptotics with increasing dimension for robust regression with applications to the bootstrap
• Limiting behavior of \(M\)-estimators of regression coefficients in high dimensional linear models. I: Scale-dependent case. II: Scale-invariant case
• Local linear estimation in partly linear models
• Asymptotics for kernel estimate of sliced inverse regression
• Robust regression: Asymptotics, conjectures and Monte Carlo
• Nonconcave penalized likelihood with a diverging number of parameters.
• Robust estimates in generalized partially linear models
• Variable selection using MM algorithms
• Shrinkage Tuning Parameter Selection with a Diverging number of Parameters
• Robustified Maximum Likelihood Estimation in Generalized Partial Linear Mixed Model for Longitudinal Data
• Weighted Wilcoxon‐Type Smoothly Clipped Absolute Deviation Method
• Root-N-Consistent Semiparametric Regression
• Generalized Partially Linear Single-Index Models
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Variable Selection for Partially Linear Models With Measurement Errors
• Asymptotic oracle properties of SCAD-penalized least squares estimators
• Tuning parameter selectors for the smoothly clipped absolute deviation method
• New Estimation and Model Selection Procedures for Semiparametric Modeling in Longitudinal Data Analysis
• Robust Estimation in Generalized Partial Linear Models for Clustered Data