Bayesian fusion estimation via \(t\) shrinkage
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Publication:2206752
DOI10.1007/s13171-019-00177-0zbMath1451.62030arXiv1812.10594OpenAlexW2965182728MaRDI QIDQ2206752
Publication date: 26 October 2020
Published in: Sankhyā. Series A (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1812.10594
Ridge regression; shrinkage estimators (Lasso) (62J07) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Bayesian inference (62F15)
Related Items (4)
Horseshoe shrinkage methods for Bayesian fusion estimation ⋮ Bayesian sparse seemingly unrelated regressions model with variable selection and covariance estimation via the horseshoe+ ⋮ Nonstationary Gaussian Process Discriminant Analysis With Variable Selection for High-Dimensional Functional Data ⋮ Unnamed Item
Uses Software
Cites Work
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- Nearly unbiased variable selection under minimax concave penalty
- Bayesian variable selection with shrinking and diffusing priors
- The Adaptive Lasso and Its Oracle Properties
- On the computational complexity of high-dimensional Bayesian variable selection
- Statistics for high-dimensional data. Methods, theory and applications.
- Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem
- Properties and refinements of the fused Lasso
- Bayesian linear regression with sparse priors
- Convergence rates of posterior distributions for non iid observations
- Bayesian cluster analysis: point estimation and credible balls (with discussion)
- Variable selection using shrinkage priors
- Convergence rates of posterior distributions.
- Adaptive estimation of a quadratic functional by model selection.
- Needles and straw in a haystack: posterior concentration for possibly sparse sequences
- Spike and slab variable selection: frequentist and Bayesian strategies
- Adaptive posterior contraction rates for the horseshoe
- Bayesian variable selection for high dimensional generalized linear models: convergence rates of the fitted densities
- Misspecification in infinite-dimensional Bayesian statistics
- Penalized regression, standard errors, and Bayesian Lassos
- A Complete Proof of Universal Inequalities for the Distribution Function of the Binomial Law
- Extended BIC for small-n-large-P sparse GLM
- Extended Bayesian information criteria for model selection with large model spaces
- The horseshoe estimator for sparse signals
- The Bayesian Lasso
- Spatial smoothing and hot spot detection for CGH data using the fused lasso
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Sparsity and Smoothness Via the Fused Lasso
- Bayesian Model Selection in High-Dimensional Settings
- Spatial Homogeneity Pursuit of Regression Coefficients for Large Datasets
- Grouping Pursuit Through a Regularization Solution Surface
- Bayesian Subset Modeling for High-Dimensional Generalized Linear Models
- Homogeneity Pursuit
- Decoupling Shrinkage and Selection in Bayesian Linear Models: A Posterior Summary Perspective
- Dirichlet–Laplace Priors for Optimal Shrinkage
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