Sparse linear regression methods for high-dimensional data often assume ...
Quantile regression and conditional density estimation can reveal struct...
In recent years, the literature on Bayesian high-dimensional variable
se...
Multivariate meta-analysis (MMA) is a powerful tool for jointly estimati...
Studying the determinants of adverse pregnancy outcomes like stillbirth ...
High-dimensional data sets have become ubiquitous in the past few decade...
We introduce a general framework for estimation and variable selection i...
The validity of conclusions from meta-analysis is potentially threatened...
The linear varying coefficient (VC) model generalizes the conventional l...
Nonparametric varying coefficient (NVC) models are widely used for model...
We introduce the spike-and-slab group lasso (SSGL) for Bayesian estimati...
We study high-dimensional Bayesian linear regression with a general beta...
We revisit the problem of simultaneously testing the means of n independ...
We consider sparse Bayesian estimation in the classical multivariate lin...
We study the well-known problem of estimating a sparse n-dimensional
unk...