The goal of this paper is to contrast and survey the major advances in two of the most
commonly used high-dimensional techniques, namely, the Lasso and horseshoe …

Since the advent of the horseshoe priors for regularisation, global–local shrinkage methods
have proved to be a fertile ground for the development of Bayesian methodology in machine …

We study the problem of recovery of matrices that are simultaneously low rank and row
and/or column sparse. Such matrices appear in recent applications in cognitive …

The goal of this paper is to contrast and survey the major advances in two of the most
commonly used high-dimensional techniques, namely, the Lasso and horseshoe …

This paper introduces an instrumental variable Bayesian shrinkage approach specifically
designed for estimating the capital asset pricing model (CAPM) while utilizing a large …

The Horseshoe is a widely used and popular continuous shrinkage prior for high-
dimensional Bayesian linear regression. Recently, regularized versions of the Horseshoe …

Gaussian mixture models are a popular tool for model-based clustering, and mixtures of
factor analyzers are Gaussian mixture models having parsimonious factor covariance …

Microbiome studies generate multivariate compositional responses, such as taxa counts,
which are strictly non-negative, bounded, residing within a simplex, and subject to unit-sum …

Regression modeling of high-dimensional data, where the number of covariates p is much
larger than the number of observations n, is increasingly common in modern statistical …

In this article, we study the problem of parameter estimation for measurement error models
by combining the Bayes method with the instrumental variable approach, deriving the …