Suboptimality of constrained least squares and improvements via non-linear predictors
Author:
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.
1. Wainwright, M.J. (2019). High-Dimensional Statistics: A Non-asymptotic Viewpoint. Cambridge Series in Statistical and Probabilistic Mathematics 48. Cambridge: Cambridge Univ. Press. 10.1017/9781108627771
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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