Robust sufficient dimension reduction via <i>α</i> -distance covariance-Reference-Cited by-同舟云学术

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Robust sufficient dimension reduction via α -distance covariance

Published:2024-02-19 Issue: Volume: Page:1-16
ISSN:1048-5252
Container-title:Journal of Nonparametric Statistics
language:en
Short-container-title:Journal of Nonparametric Statistics

Author:

Huang Hsin-Hsiung¹,Yu Feng²,Zhang Teng³

Affiliation:

1. Department of Statistics and Data Science, University of Central Florida, Orlando, FL, USA

2. Department of Mathematics, University of Minnesota Twin Cities, Minneapolis, MN, USA

3. Department of Mathematics, University of Central Florida, Orlando, FL, USA

Funder

NSF

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10485252.2024.2313137

Reference31 articles.

1. Absil, P., Mahony, R., and Sepulchre, R. (2009), Optimization Algorithms on Matrix Manifolds, Princeton, NJ: Princeton University Press. https://books.google.com/books?id=NSQGQeLN3NcC

2. Projection-like Retractions on Matrix Manifolds

3. Cook, R.D. (2009), Regression Graphics: Ideas for Studying Regressions Through Graphics, Hoboken, NJ: John Wiley & Sons.

4. Comment

5. Dalmau-Cedeno O. and Oviedo H (2017) ‘A Projection Method for Optimization Problems on the Stiefel Manifold’ in Mexican Conference on Pattern Recognition pp. 84–93.

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