A nonlinear multi-dimensional variable selection method for high dimensional data: sparse MAVE

DOI10.1016/j.csda.2008.03.003zbMath1452.62136OpenAlexW2012449009MaRDI QIDQ1023796

Xiangrong Yin, Qin Wang

Publication date: 16 June 2009

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

Full work available at URL: https://doi.org/10.1016/j.csda.2008.03.003

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Linear regression; mixed models (62J05) General nonlinear regression (62J02)

Related Items (33)

On expectile-assisted inverse regression estimation for sufficient dimension reduction ⋮ Robust functional coefficient selection for the single-index varying coefficients regression model ⋮ Robust estimation and variable selection in sufficient dimension reduction ⋮ Variable selection through adaptive MAVE ⋮ Robust direction identification and variable selection in high dimensional general single-index models ⋮ High-dimensional local polynomial regression with variable selection and dimension reduction ⋮ Dimension reduction via local rank regression ⋮ Simultaneous estimation for semi-parametric multi-index models ⋮ Robust model-free feature screening for ultrahigh dimensional surrogate data ⋮ Stable direction recovery in single-index models with a diverging number of predictors ⋮ On post dimension reduction statistical inference ⋮ An adaptive estimation of MAVE ⋮ Principal support vector machines for linear and nonlinear sufficient dimension reduction ⋮ Minimax adaptive dimension reduction for regression ⋮ The adaptive L1-penalized LAD regression for partially linear single-index models ⋮ Forward selection and estimation in high dimensional single index models ⋮ An ensemble of inverse moment estimators for sufficient dimension reduction ⋮ Feature filter for estimating central mean subspace and its sparse solution ⋮ Graph informed sliced inverse regression ⋮ Sufficient dimension reduction and variable selection for regression mean function with two types of predictors ⋮ Sparse dimension reduction for survival data ⋮ A distribution-based Lasso for a general single-index model ⋮ Minimum average variance estimation with group Lasso for the multivariate response central mean subspace ⋮ Covariate Information Matrix for Sufficient Dimension Reduction ⋮ Single-index modal regression via outer product gradients ⋮ Robust variable selection through MAVE ⋮ Robust estimation and variable selection for varying-coefficient single-index models based on modal regression ⋮ Penalized LAD Regression for Single-index Models ⋮ Modeling interactive components by coordinate kernel polynomial models ⋮ Sufficient dimension folding for a functional of conditional distribution of matrix- or array-valued objects ⋮ Variable selection and estimation for semi-parametric multiple-index models ⋮ A novel regularization method for estimation and variable selection in multi-index models ⋮ High dimensional single index models

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• Comment

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