Penalized least-squares estimation for regression coefficients in high-dimensional partially linear models

DOI10.1016/J.JSPI.2011.03.005zbMath1231.62099OpenAlexW2007168342MaRDI QIDQ645606

Huey-Fan Ni

Publication date: 10 November 2011

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2011.03.005

zbMATH Keywords

consistencyGauss-Seidel iterationsparsitygroup SCAD-penalizedlocal quadratic approximation

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Linear regression; mixed models (62J05)

Related Items (1)

Robust group non-convex estimations for high-dimensional partially linear models

Uses Software

• Excel

Cites Work

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• Profile-kernel likelihood inference with diverging number of parameters
• SCAD-penalized regression in high-dimensional partially linear models
• On the rate of convergence of the ECM algorithm
• Nonconcave penalized likelihood with a diverging number of parameters.
• Asymptotic properties of bridge estimators in sparse high-dimensional regression models
• Variable selection using MM algorithms
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Model Selection and Estimation in Regression with Grouped Variables

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