no code implementations • 6 Dec 2023 • Joonsuk Kang, Matthew Stephens
We show that this formulation also leads to a penalized decomposition of the covariance (or Gram) matrix, $\mathbf{X}^T\mathbf{X}$.
1 code implementation • 23 Aug 2022 • Youngseok Kim, Wei Wang, Peter Carbonetto, Matthew Stephens
We introduce a new empirical Bayes approach for large-scale multiple linear regression.
1 code implementation • 27 May 2021 • Peter Carbonetto, Abhishek Sarkar, ZiHao Wang, Matthew Stephens
We report on the potential for using algorithms for non-negative matrix factorization (NMF) to improve parameter estimation in topic models.
1 code implementation • 18 Dec 2018 • 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.
3 code implementations • 4 Jun 2018 • Youngseok Kim, Peter Carbonetto, Matthew Stephens, Mihai Anitescu
It is substantially faster than the interior point method, and just as accurate.
Computation Methodology
1 code implementation • 20 Feb 2018 • Wei Wang, Matthew Stephens
This yields a sparse EBMF approach - essentially a version of sparse FA/PCA - that automatically adapts the amount of sparsity to the data.
Methodology
1 code implementation • 28 Sep 2017 • David Gerard, Matthew Stephens
This yields new, powerful EB methods for analyzing genomics experiments that account for both sparse effects and unwanted variation.
Methodology
2 code implementations • 23 May 2017 • David Gerard, Matthew Stephens
In realistic simulations based on real data we found that RUVB is competitive with existing methods in terms of both power and calibration, although we also highlight the challenges of providing consistently reliable calibration among data sets.
Methodology Statistics Theory Statistics Theory 62J15 (Primary) 62F15, 62H25, 62P10 (Secondary)
1 code implementation • 25 May 2016 • Zhengrong Xing, Matthew Stephens
We describe the idea of "Adaptive Shrinkage" (ASH), a general purpose Empirical Bayes (EB) method for shrinkage estimation, and demonstrate its application to several signal denoising problems.
Methodology