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Lasso meets horseshoe: a survey (Q2292393)

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Language	Label	Description	Also known as
English	Lasso meets horseshoe: a survey	scientific article

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scholarly article

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Lasso meets horseshoe: a survey (English)

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zbMATH Open document ID

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10.1214/19-STS700

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Jyotishka Datta

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Nicholas G. Polson

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Brandon T. Willard

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Statistical Science

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publication date

3 February 2020

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full work available at URL

https://arxiv.org/abs/1706.10179

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https://projecteuclid.org/euclid.ss/1570780977

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Mathematics Subject Classification ID

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zbMATH DE Number

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zbMATH Keywords

global-local priors

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horseshoe

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horseshoe+

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hyper-parameter tuning

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Lasso

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regression

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regularization

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sparsity

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describes a project that uses

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MaRDI profile type

MaRDI publication profile

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Generalized double Pareto shrinkage

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Posterior consistency in linear models under shrinkage priors

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Needles and straw in a haystack: robust confidence for possibly sparse sequences

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Square-root lasso: pivotal recovery of sparse signals via conic programming

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Default Bayesian analysis with global-local shrinkage priors

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Prediction risk for the horseshoe regression

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The horseshoe+ estimator of ultra-sparse signals

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Dirichlet–Laplace Priors for Optimal Shrinkage

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A lasso for hierarchical interactions

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Asymptotic Bayes-optimality under sparsity of some multiple testing procedures

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Statistics for high-dimensional data. Methods, theory and applications.

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The restricted isometry property and its implications for compressed sensing

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The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).

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The Power of Convex Relaxation: Near-Optimal Matrix Completion

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The horseshoe estimator for sparse signals

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Bayesian linear regression with sparse priors

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Needles and straw in a haystack: posterior concentration for possibly sparse sequences

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Bootstrapping Lasso Estimators

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A lava attack on the recovery of sums of dense and sparse signals

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Larger posterior mode wavelet thresholding and applications

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Bayesian inference on quasi-sparse count data

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Asymptotic properties of Bayes risk for the horseshoe prior

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Compressed sensing

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Ideal spatial adaptation by wavelet shrinkage

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Adapting to Unknown Smoothness via Wavelet Shrinkage

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Microarrays, empirical Bayes and the two-groups model

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Least angle regression. (With discussion)

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Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties

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Sparse inverse covariance estimation with the graphical lasso

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Pathwise coordinate optimization

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Understanding predictive information criteria for Bayesian models

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The Variable Selection Problem

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Calibration and empirical Bayes variable selection

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Convergence rates of posterior distributions.

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Asymptotic optimality of one-group shrinkage priors in sparse high-dimensional problems

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Asymptotic properties of Bayes risk of a general class of shrinkage priors in multiple hypothesis testing under sparsity

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Computationally convenient distributional assumptions for common-value auctions

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Shrinkage regression for multivariate inference with missing data, and an application to portfolio balancing

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Inference with normal-gamma prior distributions in regression problems

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Efficient Sampling for Gaussian Linear Regression With Arbitrary Priors

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Elastic Net Regression Modeling With the Orthant Normal Prior

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Ridge Regression: Biased Estimation for Nonorthogonal Problems

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Spike and slab variable selection: frequentist and Bayesian strategies

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An Introduction to Statistical Learning

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Confidence Intervals and Hypothesis Testing for High-Dimensional Regression

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Needles and straw in haystacks: Empirical Bayes estimates of possibly sparse sequences

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Dynamic Shrinkage Processes

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Honest confidence regions for nonparametric regression

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The Graphical Horseshoe Estimator for Inverse Covariance Matrices

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Asymptotic properties of Lasso+mLS and Lasso+Ridge in sparse high-dimensional linear regression

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DOLDA: a regularized supervised topic model for high-dimensional multi-class regression

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<i>SparseNet</i>: Coordinate Descent With Nonconvex Penalties

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Bayesian Variable Selection in Linear Regression

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Tree ensembles with rule structured horseshoe regularization

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Confidence sets in sparse regression

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Comparison of Bayesian predictive methods for model selection

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Sparsity information and regularization in the horseshoe and other shrinkage priors

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Local Shrinkage Rules, Lévy Processes and Regularized Regression

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On the half-Cauchy prior for a global scale parameter

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Proximal algorithms in statistics and machine learning

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The Spike-and-Slab LASSO

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Scaled sparse linear regression

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GPU-accelerated Gibbs sampling: a case study of the horseshoe probit model

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Bayesian comparison of means of a mixed model with application to regression analysis

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The solution path of the generalized lasso

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Sparsity and Smoothness Via the Fused Lasso

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Convergence of a block coordinate descent method for nondifferentiable minimization

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On asymptotically optimal confidence regions and tests for high-dimensional models

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The horseshoe estimator: posterior concentration around nearly black vectors

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Conditions for posterior contraction in the sparse normal means problem

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Uncertainty quantification for the horseshoe (with discussion)

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Adaptive posterior contraction rates for the horseshoe

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A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis

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Model Selection and Estimation in Regression with Grouped Variables

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Nearly unbiased variable selection under minimax concave penalty

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Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models

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The Adaptive Lasso and Its Oracle Properties

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Regularization and Variable Selection Via the Elastic Net

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Sitelinks

Mathematics(1 entry)

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