Minimax estimation in multi-task regression under low-rank structures-Reference-Cited by-同舟云学术

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Minimax estimation in multi-task regression under low-rank structures

Published:2022-11-17 Issue:1 Volume:35 Page:122-144
ISSN:1048-5252
Container-title:Journal of Nonparametric Statistics
language:en
Short-container-title:Journal of Nonparametric Statistics

Author:

Bak Kwan-Young¹²^ORCID,Koo Ja-Yong³

Affiliation:

1. School of Mathematics, Statistics and Data Science, Sungshin Women's University, Seoul, Republic of Korea

2. Data Science Center, Sungshin Women's University, Seoul, Republic of Korea

3. Department of Statistics, Korea University, Seoul, Republic of Korea

Funder

Sungshin Women's University Research

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/10485252.2022.2146110

Reference15 articles.

1. Optimal selection of reduced rank estimators of high-dimensional matrices

2. Foygel, R., Horrel, M., Drton, M., and Lafferty, J. (2012), ‘Nonparametric reduced rank regression’, in Proceedings of the 25th International Conference on Neural Information Processing Systems (Vol. 1), New York: Curran Associates, Inc., pp. 1628–1636.

3. A Lower Bound on the Risks of Non-Parametric Estimates of Densities in the Uniform Metric

4. Optimal Rates of Convergence for Nonparametric Statistical Inverse Problems

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