Lasso meets horseshoe: a survey
From MaRDI portal
Publication:2292393
DOI10.1214/19-STS700zbMath1429.62308arXiv1706.10179OpenAlexW2980069674MaRDI QIDQ2292393
Brandon T. Willard, Anindya Bhadra, Nicholas G. Polson, Jyotishka Datta
Publication date: 3 February 2020
Published in: Statistical Science (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1706.10179
Related Items (27)
Bayesian variable selection in quantile regression with random effects: an application to Municipal Human Development Index ⋮ Prior normalization for certified likelihood-informed subspace detection of Bayesian inverse problems ⋮ Bayesian sparse convex clustering via global-local shrinkage priors ⋮ Unbiased approximation of posteriors via coupled particle Markov chain Monte Carlo ⋮ Unnamed Item ⋮ Empirical Bayesian Inference Using a Support Informed Prior ⋮ Bayesian variable selection for linear regression with the κ-G priors ⋮ Horseshoe Regularisation for Machine Learning in Complex and Deep Models1 ⋮ A Critical Review of LASSO and Its Derivatives for Variable Selection Under Dependence Among Covariates ⋮ The role of passing network indicators in modeling football outcomes: an application using Bayesian hierarchical models ⋮ Shrinkage with shrunken shoulders: Gibbs sampling shrinkage model posteriors with guaranteed convergence rates ⋮ A horseshoe mixture model for Bayesian screening with an application to light sheet fluorescence microscopy in brain imaging ⋮ Bayesian Change Point Detection with Spike-and-Slab Priors ⋮ Prior-Preconditioned Conjugate Gradient Method for Accelerated Gibbs Sampling in “Large n , Large p ” Bayesian Sparse Regression ⋮ A Dynamic Binary Probit Model with Time-Varying Parameters and Shrinkage Prior ⋮ Precision matrix estimation under the horseshoe-like prior-penalty dual ⋮ On global-local shrinkage priors for count data ⋮ High-dimensional Bayesian network classification with network global-local shrinkage priors ⋮ Modeling time-varying parameters using artificial neural networks: a GARCH illustration ⋮ Dealing with Measurement Uncertainties as Nuisance Parameters in Bayesian Model Calibration ⋮ The horseshoe-like regularization for feature subset selection ⋮ Bayesian deconvolution and quantification of metabolites from \(J\)-resolved NMR spectroscopy ⋮ A Bayesian time-varying effect model for behavioral mHealth data ⋮ Shared Bayesian variable shrinkage in multinomial logistic regression ⋮ Sparse linear mixed model selection via streamlined variational Bayes ⋮ Shrinkage priors for Bayesian penalized regression ⋮ Discussion to: Bayesian graphical models for modern biological applications by Y. Ni, V. Baladandayuthapani, M. Vannucci and F.C. Stingo
Uses Software
Cites Work
- A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis
- Shrinkage regression for multivariate inference with missing data, and an application to portfolio balancing
- Sparsity information and regularization in the horseshoe and other shrinkage priors
- On asymptotically optimal confidence regions and tests for high-dimensional models
- Nearly unbiased variable selection under minimax concave penalty
- A lava attack on the recovery of sums of dense and sparse signals
- Sparse inverse covariance estimation with the graphical lasso
- The Adaptive Lasso and Its Oracle Properties
- Understanding predictive information criteria for Bayesian models
- Conditions for posterior contraction in the sparse normal means problem
- A lasso for hierarchical interactions
- Asymptotic properties of Lasso+mLS and Lasso+Ridge in sparse high-dimensional linear regression
- The horseshoe estimator: posterior concentration around nearly black vectors
- Asymptotic properties of Bayes risk of a general class of shrinkage priors in multiple hypothesis testing under sparsity
- Statistics for high-dimensional data. Methods, theory and applications.
- The solution path of the generalized lasso
- Asymptotic Bayes-optimality under sparsity of some multiple testing procedures
- Bayesian linear regression with sparse priors
- Microarrays, empirical Bayes and the two-groups model
- Asymptotic properties of Bayes risk for the horseshoe prior
- The restricted isometry property and its implications for compressed sensing
- Larger posterior mode wavelet thresholding and applications
- Computationally convenient distributional assumptions for common-value auctions
- The horseshoe+ estimator of ultra-sparse signals
- Asymptotic optimality of one-group shrinkage priors in sparse high-dimensional problems
- Uncertainty quantification for the horseshoe (with discussion)
- Tree ensembles with rule structured horseshoe regularization
- Proximal algorithms in statistics and machine learning
- Honest confidence regions for nonparametric regression
- Convergence rates of posterior distributions.
- Least angle regression. (With discussion)
- Needles and straw in haystacks: Empirical Bayes estimates of possibly sparse sequences
- Needles and straw in a haystack: posterior concentration for possibly sparse sequences
- DOLDA: a regularized supervised topic model for high-dimensional multi-class regression
- Needles and straw in a haystack: robust confidence for possibly sparse sequences
- GPU-accelerated Gibbs sampling: a case study of the horseshoe probit model
- Comparison of Bayesian predictive methods for model selection
- Spike and slab variable selection: frequentist and Bayesian strategies
- Adaptive posterior contraction rates for the horseshoe
- Confidence sets in sparse regression
- Pathwise coordinate optimization
- The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
- On the half-Cauchy prior for a global scale parameter
- Inference with normal-gamma prior distributions in regression problems
- Calibration and empirical Bayes variable selection
- Posterior consistency in linear models under shrinkage priors
- Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
- Bootstrapping Lasso Estimators
- Square-root lasso: pivotal recovery of sparse signals via conic programming
- SparseNet: Coordinate Descent With Nonconvex Penalties
- Adapting to Unknown Smoothness via Wavelet Shrinkage
- Scaled sparse linear regression
- Elastic Net Regression Modeling With the Orthant Normal Prior
- Efficient Sampling for Gaussian Linear Regression With Arbitrary Priors
- The horseshoe estimator for sparse signals
- Bayesian Variable Selection in Linear Regression
- Ideal spatial adaptation by wavelet shrinkage
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- The Variable Selection Problem
- Sparsity and Smoothness Via the Fused Lasso
- The Spike-and-Slab LASSO
- Generalized double Pareto shrinkage
- An Introduction to Statistical Learning
- Dynamic Shrinkage Processes
- The Power of Convex Relaxation: Near-Optimal Matrix Completion
- Regularization and Variable Selection Via the Elastic Net
- Dirichlet–Laplace Priors for Optimal Shrinkage
- Prediction risk for the horseshoe regression
- Default Bayesian analysis with global-local shrinkage priors
- Bayesian inference on quasi-sparse count data
- Model Selection and Estimation in Regression with Grouped Variables
- Bayesian comparison of means of a mixed model with application to regression analysis
- Ridge Regression: Biased Estimation for Nonorthogonal Problems
- Local Shrinkage Rules, Lévy Processes and Regularized Regression
- Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models
- Compressed sensing
- Convergence of a block coordinate descent method for nondifferentiable minimization
- The Graphical Horseshoe Estimator for Inverse Covariance Matrices
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
This page was built for publication: Lasso meets horseshoe: a survey