Bayesian statistics is an approach to data analysis based on Bayes' theorem, where
available knowledge about parameters in a statistical model is updated with the information …

Accurate genetic prediction of complex traits can facilitate disease screening, improve early
intervention, and aid in the development of personalized medicine. Genetic prediction of …

Polygenic risk scores (PRS) have shown promise in predicting human complex traits and
diseases. Here, we present PRS-CS, a polygenic prediction method that infers posterior …

The horseshoe prior has proven to be a noteworthy alternative for sparse Bayesian
estimation, but has previously suffered from two problems. First, there has been no …

Despite the wide adoption of spike-and-slab methodology for Bayesian variable selection,
its potential for penalized likelihood estimation has largely been overlooked. In this article …

In this note we derive a simple Bayesian sampler for linear regression with the horseshoe
hierarchy. A new interpretation of the horseshoe model is presented, and extensions to …

In linear regression problems with many predictors, penalized regression techniques are
often used to guard against overfitting and to select variables relevant for predicting an …

This paper argues that the half-Cauchy distribution should replace the inverse-Gamma
distribution as a default prior for a top-level scale parameter in Bayesian hierarchical …

Penalized regression methods, such as L 1 regularization, are routinely used in high-
dimensional applications, and there is a rich literature on optimality properties under sparsity …

We propose a generalized double Pareto prior for Bayesian shrinkage estimation and
inferences in linear models. The prior can be obtained via a scale mixture of Laplace or …