A brief review of linear sufficient dimension reduction through optimization-Reference-Cited by-同舟云学术

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A brief review of linear sufficient dimension reduction through optimization

Published:2021-03 Issue: Volume:211 Page:154-161
ISSN:0378-3758
Container-title:Journal of Statistical Planning and Inference
language:en
Short-container-title:Journal of Statistical Planning and Inference

Author:

Dong Yuexiao^ORCID

Publisher

Elsevier BV

Subject

Applied Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability

Reference59 articles.

1. Sufficient dimension reduction via principal ℓ-q support vector machine;Artemiou;Electron. J. Stat.,2016

2. Manopt, a matlab toolbox for optimization on manifolds;Boumal;J. Mach. Learn. Res.,2014

3. Kernel feature selection via conditional covariance minimization;Chen,2018

4. Central quantile subspace;Christou;Stat. Comput.,2020

5. Regression Graphics: Ideas for Studying Regressions Through Graphics;Cook,1998

Cited by 14 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Model-free controlled variable selection via data splitting;SCIENTIA SINICA Mathematica;2024-08-01

2. A note on marginal coordinate test in sufficient dimension reduction;Statistics & Probability Letters;2024-01

3. A selective review of sufficient dimension reduction for multivariate response regression;Journal of Statistical Planning and Inference;2023-09

4. Projection expectile regression for sufficient dimension reduction;Computational Statistics & Data Analysis;2023-04

5. A new reproducing kernel‐based nonlinear dimension reduction method for survival data;Scandinavian Journal of Statistics;2023-03-21

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