Graph signal denoising using \(t\)-shrinkage priors
From MaRDI portal
Publication:2123272
DOI10.1016/j.jspi.2021.12.012OpenAlexW4206468850MaRDI QIDQ2123272
Sayantan Banerjee, Weining Shen
Publication date: 8 April 2022
Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2012.13696
adaptive estimationposterior contraction rateBayesian shrinkagedepth first searchgraph denoisingheavy tailed prior
Related Items
Cites Work
- Unnamed Item
- Unnamed Item
- Nonlinear total variation based noise removal algorithms
- Community detection in networks with node features
- Bayesian linear regression with sparse priors
- Risk bounds for model selection via penalization
- Spectral clustering via sparse graph structure learning with application to proteomic signaling networks in cancer
- Nonparametric shape-restricted regression
- Approximate \(\ell_0\)-penalized estimation of piecewise-constant signals on graphs
- On estimation of isotonic piecewise constant signals
- Minimal penalties for Gaussian model selection
- Penalized regression, standard errors, and Bayesian Lassos
- Generalized methods and solvers for noise removal from piecewise constant signals. I. Background theory
- Generalized methods and solvers for noise removal from piecewise constant signals. II. New methods
- The horseshoe estimator for sparse signals
- The DFS Fused Lasso: Linear-Time Denoising over General Graphs
- Sparsity and Smoothness Via the Fused Lasso
- Spatial Homogeneity Pursuit of Regression Coefficients for Large Datasets
- Collective dynamics of ‘small-world’ networks
- Dirichlet–Laplace Priors for Optimal Shrinkage
- Depth-First Search and Linear Graph Algorithms
- Exact Bayesian regression of piecewise constant functions