Sufficient dimension reduction through informative predictor subspace

DOI10.1080/02331888.2016.1148151zbMath1356.62055OpenAlexW2319026050MaRDI QIDQ2953449

Jae Keun Yoo

Publication date: 4 January 2017

Published in: Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331888.2016.1148151

zbMATH Keywords

regressionsufficient dimension reductioncentral subspacenumerical studieslinearity conditioninformative predictor subspace

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Characterization and structure theory for multivariate probability distributions; copulas (62H05)

Related Items (3)

Fused clustering mean estimation of central subspace ⋮ Feature filter for estimating central mean subspace and its sparse solution ⋮ Projective resampling estimation of informative predictor subspace for multivariate regression

Cites Work

• On the extension of sliced average variance estimation to multivariate regression
• Multivariate seeded dimension reduction
• On almost linearity of low dimensional projections from high dimensional data
• Cluster-based estimation for sufficient dimension reduction
• Dimension reduction for nonelliptically distributed predictors
• A characterization of spherical distributions
• Sufficient dimension reduction in multivariate regressions with categorical predictors
• Sufficient Dimension Reduction for Censored Regressions
• Simultaneous Equations and Canonical Correlation Theory
• Save: a method for dimension reduction and graphics in regression
• An Adaptive Estimation of Dimension Reduction Space
• Chi-squared tests inkth-moment sufficient dimension reduction
• Fused Estimators of the Central Subspace in Sufficient Dimension Reduction
• Sufficient Dimension Reduction via Inverse Regression

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