Model-Free Variable Selection

DOI10.1111/j.1467-9868.2005.00502.xzbMath1069.62053OpenAlexW1986349898MaRDI QIDQ5313590

Lexin Li, R. Dennis Cook, Christopher J. Nachtsheim

Publication date: 1 September 2005

Published in: Journal of the Royal Statistical Society Series B: Statistical Methodology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1467-9868.2005.00502.x

zbMATH Keywords

model selectionsimulationssufficient dimension reductionsliced inverse regressionstepwise regressionenvironmental contamination

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Applications of statistics to environmental and related topics (62P12) Linear inference, regression (62J99)

Related Items

Sparse sufficient dimension reduction using optimal scoring, Model-free variable selection for conditional mean in regression, Multiple Loci Mapping via Model-free Variable Selection, A model-free variable selection method for reducing the number of redundant variables, Data-driven algorithms for dimension reduction in causal inference, On dual model-free variable selection with two groups of variables, Variable selection through adaptive MAVE, Sparse SIR: optimal rates and adaptive estimation, Dimension estimation in sufficient dimension reduction: a unifying approach, An Inverse-regression Method of Dependent Variable Transformation for Dimension Reduction with Non-linear Confounding, Coordinate-independent sparse sufficient dimension reduction and variable selection, High-dimensional local polynomial regression with variable selection and dimension reduction, A conditional independence test for dependent data based on maximal conditional correlation, Dimension reduction based linear surrogate variable approach for model free variable selection, A link-free method for testing the significance of predictors, Analyzing large datasets with bootstrap penalization, Refined Generalization Bounds of Gradient Learning over Reproducing Kernel Hilbert Spaces, A link-free sparse group variable selection method for single-index model, Nonparametric significance testing and group variable selection, Variable selection for general index models via sliced inverse regression, 3 dimensional parallel coordinates plot and its use for variable selection, Subset selection algorithm based on mutual information, Shrinkage Inverse Regression Estimation for Model-Free Variable Selection, Dimension reduction in survival regressions with censored data via an imputed spline approach, Ranking the importance of variables in nonlinear system identification, Rodeo: Sparse, greedy nonparametric regression, High-dimensional regression analysis with treatment comparisons, Kernel Knockoffs Selection for Nonparametric Additive Models, Robust variable selection through MAVE, New approaches to model-free dimension reduction for bivariate regression, A nonlinear multi-dimensional variable selection method for high dimensional data: sparse MAVE, Robust Variable and Interaction Selection for Logistic Regression and General Index Models, Influence Functions for Dimension Reduction Methods: An Example Influence Study of Principal Hessian Direction Analysis, Variable selection and estimation for semi-parametric multiple-index models

Uses Software

• rms

Cites Work

• Cluster-based estimation for sufficient dimension reduction
• Nonlinear confounding in high-dimensional regression
• Model selection in nonparametric regression
• Sufficient dimension reduction and graphics in regression
• Testing predictor contributions in sufficient dimension reduction.
• A note On outlier sensitivity of Sliced Inverse Regression
• Search for significant variables in nonparametric additive regression
• The Covariance Inflation Criterion for Adaptive Model Selection
• Adaptive Model Selection
• Variable Selection in Data Mining
• Regression modeling strategies. With applications to linear models, logistic regression and survival analysis
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item