Adaptive risk bounds in univariate total variation denoising and trend filtering
DOI10.1214/18-AOS1799zbMath1439.62100arXiv1702.05113MaRDI QIDQ2176616
Sabyasachi Chatterjee, Donovan Lieu, Bodhisattva Sen, Adityanand Guntuboyina
Publication date: 5 May 2020
Published in: The Annals of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1702.05113
subdifferentialtangent conenonparametric function estimationrisk boundsdiscrete splinesadaptive splinesfat shatteringhigher-order total variation regularizationmetric entropy bounds
Inference from stochastic processes and prediction (62M20) Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Numerical computation using splines (65D07) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)
Related Items
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Nonlinear total variation based noise removal algorithms
- Sharp MSE bounds for proximal denoising
- On the prediction performance of the Lasso
- Statistics for high-dimensional data. Methods, theory and applications.
- Combinatorics of random processes and sections of convex bodies
- Splines in higher order TV regularization
- Variable bandwidth kernel estimators of regression curves
- Multivariate adaptive regression splines
- Locally adaptive regression splines
- Optimal spatial adaptation to inhomogeneous smoothness: An approach based on kernel estimates with variable bandwidth selectors
- On spatially adaptive estimation of nonparametric regression
- On the total variation regularized estimator over a class of tree graphs
- Nonparametric shape-restricted regression
- Sharp oracle inequalities for least squares estimators in shape restricted regression
- Minimax estimation via wavelet shrinkage
- Adaptive risk bounds in univariate total variation denoising and trend filtering
- Adaptive piecewise polynomial estimation via trend filtering
- Nonparametric Estimation under Shape Constraints
- $\ell_1$ Trend Filtering
- Locally Adaptive Bandwidth Choice for Kernel Regression Estimators
- Ideal spatial adaptation by wavelet shrinkage
- On tight bounds for the Lasso
- Smoothers for Discontinuous Signals
- Spatially Adaptive Regression Splines and Accurate Knot Selection Schemes
- Multiple Change-Point Estimation With a Total Variation Penalty
- Minimax Rates of Estimation for High-Dimensional Linear Regression Over $\ell_q$-Balls
- Corrupted Sensing: Novel Guarantees for Separating Structured Signals
- Spatially adaptive smoothing splines
- Discrete Splines via Mathematical Programming
This page was built for publication: Adaptive risk bounds in univariate total variation denoising and trend filtering