340 citations · 1k across the 21 of their papers we have counts for
22 papers
A flexible empirical Bayes approach to multiple linear regression and connections with penalized regression
Youngseok Kim, Wei Wang, Peter Carbonetto +1
We introduce a new empirical Bayes approach for large-scale multiple linear regression. Our approach combines two key ideas: (i) the use of flexible "adaptive shrinkage" priors, wh…
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…
Multi-scale Poisson process approaches for differential expression analysis of high-throughput sequencing data
Heejung Shim, Zhengrong Xing, Ester Pantaleo +3
Estimating and testing for differences in molecular phenotypes (e.g. gene expression, chromatin accessibility, transcription factor binding) across conditions is an important part…
Empirical Bayes estimation of normal means, accounting for uncertainty in estimated standard errors
Mengyin Lu, Matthew Stephens
We consider Empirical Bayes (EB) estimation in the normal means problem, when the standard deviations of the observations are not known precisely, but estimated with error -- which…
Solving the Empirical Bayes Normal Means Problem with Correlated Noise
Lei Sun, Matthew Stephens
The Normal Means problem plays a fundamental role in many areas of modern high-dimensional statistics, both in theory and practice. And the Empirical Bayes (EB) approach to solving…
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…