32 citations · 133 across the 15 of their papers we have counts for
16 papers
Empirical Bayes Shrinkage of Functional Effects, with Application to Analysis of Dynamic eQTLs
Ziang Zhang, Peter Carbonetto, Matthew Stephens
We introduce functional adaptive shrinkage (FASH), an empirical Bayes method for joint analysis of observation units in which each unit estimates an effect function at several valu…
A New Family of Poisson Non-negative Matrix Factorization Methods Using the Shifted Log Link
Eric Weine, Peter Carbonetto, Rafael A. Irizarry +1
Poisson non-negative matrix factorization (NMF) is a widely used method to find interpretable "parts-based" decompositions of count data. While many variants of Poisson NMF exist,…
Bayesian variable selection in a Cox proportional hazards model with the "Sum of Single Effects" prior
Yunqi Yang, Karl Tayeb, Peter Carbonetto +3
Motivated by genetic fine-mapping applications, we introduce a new approach to Bayesian variable selection regression (BVSR) for time-to-event (TTE) outcomes. This new approach is…
Covariate-moderated Empirical Bayes Matrix Factorization
William R. P. Denault, Karl Tayeb, Peter Carbonetto +2
Matrix factorization is a fundamental method in statistics and machine learning for inferring and summarizing structure in multivariate data. Modern data sets often come with "side…
Gradient-based optimization for variational empirical Bayes multiple regression
Saikat Banerjee, Peter Carbonetto, Matthew Stephens
Variational empirical Bayes (VEB) methods provide a practically attractive approach to fitting large, sparse, multiple regression models. These methods usually use coordinate ascen…
Improved methods for empirical Bayes multivariate multiple testing and effect size estimation
Yunqi Yang, Peter Carbonetto, David Gerard +1
Estimating the sharing of genetic effects across different conditions is important to many statistical analyses of genomic data. The patterns of sharing arising from these data are…
A flexible model for correlated count data, with application to multi-condition differential expression analyses of single-cell RNA sequencing data
Yusha Liu, Peter Carbonetto, Michihiro Takahama +4
Detecting differences in gene expression is an important part of single-cell RNA sequencing experiments, and many statistical methods have been developed for this aim. Most differe…
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…
Non-negative matrix factorization algorithms generally improve topic model fits
Peter Carbonetto, Abhishek Sarkar, Zihao Wang +1
In an effort to develop topic modeling methods that can be quickly applied to large data sets, we revisit the problem of maximum-likelihood estimation in topic models. It is known,…
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…
Flexible signal denoising via flexible empirical Bayes shrinkage
Zhengrong Xing, Peter Carbonetto, Matthew Stephens
Signal denoising---also known as non-parametric regression---is often performed through shrinkage estimation in a transformed (e.g., wavelet) domain; shrinkage in the transformed d…
Polygenic Modeling with Bayesian Sparse Linear Mixed Models
Xiang Zhou, Peter Carbonetto, Matthew Stephens
Both linear mixed models (LMMs) and sparse regression models are widely used in genetics applications, including, recently, polygenic modeling in genome-wide association studies. T…
Integrated analysis of variants and pathways in genome-wide association studies using polygenic models of disease
Peter Carbonetto, Matthew Stephens
Many common diseases are highly polygenic, modulated by a large number genetic factors with small effects on susceptibility to disease. These small effects are difficult to map rel…
Nonparametric Bayesian Logic
Peter Carbonetto, Jacek Kisynski, Nando de Freitas +1
The Bayesian Logic (BLOG) language was recently developed for defining first-order probability models over worlds with unknown numbers of objects. It handles important problems in…