High-throughput technologies have been used to generate a large amount of omics data. In
the past, single-level analysis has been extensively conducted where the omics …

The limitations of the well‐known LASSO regression as a variable selector are tested when
there exists dependence structures among covariates. We analyse both the classic situation …

We provide a review of prior distributions for objective Bayesian analysis. We start by
examining some foundational issues and then organize our exposition into priors for: i) …

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 …

We study a mean-field spike and slab variational Bayes (VB) approximation to Bayesian
model selection priors in sparse high-dimensional linear regression. Under compatibility …

Network psychometrics is a new direction in psychological research that conceptualizes
psychological constructs as systems of interacting variables. In network analysis, variables …

We develop the sparse VAE for unsupervised representation learning on high-dimensional
data. The sparse VAE learns a set of latent factors (representations) which summarize the …

A review of theoretical concepts and diverse applications of sparse representations and
compressive sampling (CS) approaches in engineering mechanics problems is provided …

Mediation analysis investigates the intermediate mechanism through which an exposure
exerts its influence on the outcome of interest. Mediation analysis is becoming increasingly …

In recent years, the crude oil market has entered a new period of development and the core
influence factors of crude oil have also been a change. Thus, we develop a new research …