Concentration behavior of the penalized least squares estimator
From MaRDI portal
Publication:6089165
DOI10.1111/stan.12123arXiv1511.08698OpenAlexW2963652843MaRDI QIDQ6089165
Publication date: 14 December 2023
Published in: Statistica Neerlandica (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1511.08698
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A new perspective on least squares under convex constraint
- Oracle inequalities in empirical risk minimization and sparse recovery problems. École d'Été de Probabilités de Saint-Flour XXXVIII-2008.
- Maxiset in sup-norm for kernel estimators
- Lectures on empirical processes. Theory and statistical applications.
- Estimating a regression function
- Information-theoretic determination of minimax rates of convergence
- Minimax or maxisets?
- On concentration for (regularized) empirical risk minimization
- Bounds on the prediction error of penalized least squares estimators with convex penalty
- Optimal global rates of convergence for nonparametric regression
- Weak convergence and empirical processes. With applications to statistics
- Optimal upper and lower bounds for the true and empirical excess risks in heteroscedastic least-squares regression
- Optimal model selection in heteroscedastic regression using piecewise polynomial functions
- Empirical minimization
- Slope heuristics and V-Fold model selection in heteroscedastic regression using strongly localized bases
- Smoothing spline ANOVA models
This page was built for publication: Concentration behavior of the penalized least squares estimator