On the optimality of sliced inverse regression in high dimensions

DOI10.1214/19-AOS1813zbMath1464.62337arXiv1701.06009MaRDI QIDQ2656585

Dongming Huang, Xinran Li, Jun S. Liu, Qian Lin

Publication date: 11 March 2021

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1701.06009

zbMATH Keywords

sufficient dimension reductionoptimal ratessliced inverse regression (SIR)semidefinite positive programmingsparse SIR

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Minimax procedures in statistical decision theory (62C20) General nonlinear regression (62J02) Applications of mathematical programming (90C90) General considerations in statistical decision theory (62C05)

Related Items (11)

A selective overview of sparse sufficient dimension reduction ⋮ Online sparse sliced inverse regression for high-dimensional streaming data ⋮ Sparse Sliced Inverse Regression via Cholesky Matrix Penalization ⋮ Data-guided Treatment Recommendation with Feature Scores ⋮ Functional sufficient dimension reduction through average Fréchet derivatives ⋮ Unnamed Item ⋮ Sparse SIR: optimal rates and adaptive estimation ⋮ Self-supervised Metric Learning in Multi-View Data: A Downstream Task Perspective ⋮ Subspace Estimation with Automatic Dimension and Variable Selection in Sufficient Dimension Reduction ⋮ Likelihood-based surrogate dimension reduction ⋮ High-dimensional index volatility models via Stein's identity

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• Comment

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