32 citations · 133 across the 15 of their papers we have counts for
3 papers · 1 filter
ebnm: An R Package for Solving the Empirical Bayes Normal Means Problem Using a Variety of Prior Families
Jason Willwerscheid, Peter Carbonetto, Matthew Stephens
The empirical Bayes normal means (EBNM) model is important to many areas of statistics, including (but not limited to) multiple testing, wavelet denoising, and gene expression anal…
A fast algorithm for maximum likelihood estimation of mixture proportions using sequential quadratic programming
Youngseok Kim, Peter Carbonetto, Matthew Stephens +1
Maximum likelihood estimation of mixture proportions has a long history, and continues to play an important role in modern statistics, including in development of nonparametric emp…
varbvs: Fast Variable Selection for Large-scale Regression
Peter Carbonetto, Xiang Zhou, Matthew Stephens
We introduce varbvs, a suite of functions written in R and MATLAB for regression analysis of large-scale data sets using Bayesian variable selection methods. We have developed nume…