Sufficient dimension reduction and variable selection for regression mean function with two types of predictors

DOI10.1016/J.SPL.2008.03.030zbMath1256.62032OpenAlexW2025571699MaRDI QIDQ952880

Qin Wang, Xiangrong Yin

Publication date: 14 November 2008

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2008.03.030

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Linear regression; mixed models (62J05) Generalized linear models (logistic models) (62J12)

Related Items (5)

Sufficient dimension reduction via principal L\(q\) support vector machine ⋮ A Sliced Inverse Regression Approach for a Stratified Population ⋮ Partial dynamic dimension reduction for conditional mean in regression ⋮ Longitudinal data analysis using sufficient dimension reduction method ⋮ Sufficient dimension reduction for conditional quantiles with alternative types of data

Cites Work

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• A nonlinear multi-dimensional variable selection method for high dimensional data: sparse MAVE
• Dimension reduction for the conditional mean in regressions with categorical predictors
• Dimension reduction for conditional mean in regression
• Sufficient dimension reduction in regressions with categorical predictors
• On Principal Hessian Directions for Data Visualization and Dimension Reduction: Another Application of Stein's Lemma
• Generalized Partially Linear Single-Index Models
• A Model-Free Test for Reduced Rank in Multivariate Regression
• Using the Bootstrap to Select One of a New Class of Dimension Reduction Methods
• Identifying Regression Outliers and Mixtures Graphically
• An Adaptive Estimation of Dimension Reduction Space
• A note on shrinkage sliced inverse regression
• Sparse sufficient dimension reduction
• NON-PARAMETRIC ESTIMATION OF DIRECTION IN SINGLE-INDEX MODELS WITH CATEGORICAL PREDICTORS

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