Advanced search
Publication Cover
Journal of Computational and Graphical Statistics Volume 23, 2014 - Issue 4
Submit an article Journal homepage
413
Views
13
CrossRef citations to date
0
Altmetric
ARTICLES: Regression and Variable Selection

Sufficient Dimension Folding for Regression Mean Function

Yuan Xue
&
Xiangrong Yin
Pages 1028-1043 | Received 01 Feb 2013, Published online: 20 Oct 2014

Citations (13)

Keep up to date with the latest research on this topic with citation updates for this article.

Subscribe to citation updates

Read on this site (4)

Hong-Fan Zhang. (2023) A varying coefficient model with matrix valued covariates. Journal of Nonparametric Statistics 0:0, pages 1-33.
Read now
Zeda Li & Yuexiao Dong. (2021) Model-Free Variable Selection With Matrix-Valued Predictors. Journal of Computational and Graphical Statistics 30:1, pages 171-181.
Read now
Joni Virta, Bing Li, Klaus Nordhausen & Hannu Oja. (2018) JADE for Tensor-Valued Observations. Journal of Computational and Graphical Statistics 27:3, pages 628-637.
Read now
Yuan Xue & Xiangrong Yin. (2015) Sufficient dimension folding for a functional of conditional distribution of matrix- or array-valued objects. Journal of Nonparametric Statistics 27:2, pages 253-269.
Read now

Articles from other publishers (9)

Hossein Baghishani & Jamil Ownuk. 2022. Innovations in Multivariate Statistical Modeling. Innovations in Multivariate Statistical Modeling 169 185 .
Yuanwen Wang, Yuan Xue, Qingcong Yuan & Xiangrong Yin. 2021. Festschrift in Honor of R. Dennis Cook. Festschrift in Honor of R. Dennis Cook 127 165 .
Bei Jiang, Eva Petkova, Thaddeus Tarpey & R. Todd Ogden. (2019) A Bayesian approach to joint modeling of matrix‐valued imaging data and treatment outcome with applications to depression studies. Biometrics 76:1, pages 87-97.
Crossref
Wenhui Sheng & Qingcong Yuan. (2019) Sufficient dimension folding in regression via distance covariance for matrix‐valued predictors. Statistical Analysis and Data Mining: The ASA Data Science Journal 13:1, pages 71-82.
Crossref
Shanshan Ding & R. Dennis Cook. (2018) Matrix Variate Regressions and Envelope Models. Journal of the Royal Statistical Society Series B: Statistical Methodology 80:2, pages 387-408.
Crossref
Joni Virta, Bing Li, Klaus Nordhausen & Hannu Oja. (2017) Independent component analysis for tensor-valued data. Journal of Multivariate Analysis 162, pages 172-192.
Crossref
Hung Hung & Henry Horng-Shing Lu. (2017) A review on the generalization of sufficient dimension reduction methods with the additional information. Wiley Interdisciplinary Reviews: Computational Statistics 9:4, pages e1401.
Crossref
Yuan Xue, Xiangrong Yin & Xiaolin Jiang. (2016) Ensemble sufficient dimension folding methods for analyzing matrix-valued data. Computational Statistics & Data Analysis 103, pages 193-205.
Crossref
Shanshan Ding & R. Dennis Cook. (2015) Higher-order sliced inverse regressions. Wiley Interdisciplinary Reviews: Computational Statistics 7:4, pages 249-257.
Crossref

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Order Reprints Request Corporate Permissions

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

Request Academic Permissions

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.