Suboptimality of constrained least squares and improvements via non-linear predictors-Reference-Cited by-同舟云学术

Suboptimality of constrained least squares and improvements via non-linear predictors

Published:2023-02-01 Issue:1 Volume:29 Page:
ISSN:1350-7265
Container-title:Bernoulli
language:
Short-container-title:Bernoulli

Author:

Vaškevičius Tomas¹,Zhivotovskiy Nikita²

Affiliation:

1. Department of Statistics, University of Oxford, Oxford, OX1 3LB, United Kingdom

2. Department of Mathematics, ETH Zürich, Rämistrasse 101, 8092 Zürich, Switzerland

Publisher

Bernoulli Society for Mathematical Statistics and Probability

Subject

Statistics and Probability

Reference64 articles.

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2. Vershynin, R. (2018). High-Dimensional Probability: An Introduction with Applications in Data Science. Cambridge Series in Statistical and Probabilistic Mathematics 47. Cambridge: Cambridge Univ. Press. 10.1017/9781108231596

3. Györfi, L., Kohler, M., Krzyżak, A. and Walk, H. (2002). A Distribution-Free Theory of Nonparametric Regression. Springer Series in Statistics. New York: Springer. 10.1007/b97848

4. Chatterjee, S. (2014). A new perspective on least squares under convex constraint. Ann. Statist. 42 2340–2381. 10.1214/14-AOS1254

5. Nemirovski, A. (2000). Topics in non-parametric statistics. In Lectures on Probability Theory and Statistics (Saint-Flour, 1998). Lecture Notes in Math. 1738 85–277. Berlin: Springer.

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