A sparse eigen-decomposition estimation in semiparametric regression

DOI10.1016/j.csda.2009.10.011zbMath1464.62202OpenAlexW2046788079MaRDI QIDQ962349

Zhou Yu, Li-ping Zhu, Li Xing Zhu

Publication date: 6 April 2010

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2009.10.011

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08) Linear regression; mixed models (62J05)

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Cites Work

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• The Adaptive Lasso and Its Oracle Properties
• Successive direction extraction for estimating the central subspace in a multiple-index regres\-sion
• Contour projected dimension reduction
• Asymptotics for kernel estimate of sliced inverse regression
• Asymptotics for Lasso-type estimators.
• Dimension reduction for conditional mean in regression
• Nonconcave penalized likelihood with a diverging number of parameters.
• Least angle regression. (With discussion)
• On the asymptotics of constrained \(M\)-estimation
• On kernel method for sliced average variance estimation
• Determining the dimension of iterative Hessian transformation
• Contour regression: a general approach to dimension reduction
• Fourier Methods for Estimating the Central Subspace and the Central Mean Subspace in Regression
• Determining the dimension of the central subspace and central mean subspace
• On Directional Regression for Dimension Reduction
• Unified LASSO Estimation by Least Squares Approximation
• Sliced Inverse Regression for Dimension Reduction
• On Principal Hessian Directions for Data Visualization and Dimension Reduction: Another Application of Stein's Lemma
• Determining the Dimensionality in Sliced Inverse Regression
• Extending Sliced Inverse Regression
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Using the Bootstrap to Select One of a New Class of Dimension Reduction Methods
• Assessing the Number of Linear Components in a General Regression Problem
• Interactive Tree-Structured Regression via Principal Hessian Directions
• An Adaptive Estimation of Dimension Reduction Space
• Tuning parameter selectors for the smoothly clipped absolute deviation method
• Sufficient Dimension Reduction via Inverse Regression
• On Sliced Inverse Regression With High-Dimensional Covariates