Models with dimension more than the available sample size are now commonly used in
various applications. A sensible inference is possible using a lower-dimensional structure. In …

The quantile varying coefficient (VC) model can flexibly capture dynamical patterns of
regression coefficients. In addition, due to the quantile check loss function, it is robust …

Variational Bayes (VB) is a popular scalable alternative to Markov chain Monte Carlo for
Bayesian inference. We study a mean-field spike and slab VB approximation of widely used …

We propose a scalable variational Bayes method for statistical inference for a single or low-
dimensional subset of the coordinates of a high-dimensional parameter in sparse linear …

We propose a general framework using spike-and-slab prior distributions to aid with the
development of high-dimensional Bayesian inference. Our framework allows inference with …

Nonparametric varying coefficient models are useful for studying the time-dependent effects
of variables. Many procedures have been developed for estimation and variable selection in …

In high-dimensions, the prior tails can have a significant effect on both posterior computation
and asymptotic concentration rates. To achieve optimal rates while keeping the posterior …

Datasets are growing not just in size but in complexity, creating a demand for rich models
and quantification of uncertainty. Bayesian methods are an excellent fit for this demand, but …

We propose a variational Bayesian (VB) procedure for high-dimensional linear model
inferences with heavy tail shrinkage priors, such as student-t prior. Theoretically, we …