194 citations · 308 across the 10 of their papers we have counts for
10 papers
MixTwice: large-scale hypothesis testing for peptide arrays by variance mixing
Zihao Zheng, Aisha M. Mergaert, Irene M. Ong +2
Peptide microarrays have emerged as a powerful technology in immunoproteomics as they provide a tool to measure the abundance of different antibodies in patient serum samples. The…
Bayes Optimal Informer Sets for Early-Stage Drug Discovery
Peng Yu, Spencer S. Ericksen, Anthony Gitter +1
An important experimental design problem in early-stage drug discovery is how to prioritize available compounds for testing when very little is known about the target protein. Info…
Random weighting in LASSO regression
Tun Lee Ng, Michael A. Newton
We establish statistical properties of random-weighting methods in LASSO regression under different regularization parameters and suitable regularity conditions. The random-w…
Dimension constraints improve hypothesis testing for large-scale, graph-associated, brain-image data
TIen Vo, Vamsi Ithapu, Vikas Singh +1
For large-scale testing with graph-associated data, we present an empirical Bayes mixture technique to score local false discovery rates. Compared to empirical Bayes procedures tha…
Weighted Bayesian Bootstrap for Scalable Bayes
Michael Newton, Nicholas G. Polson, Jianeng Xu
We develop a weighted Bayesian Bootstrap (WBB) for machine learning and statistics. WBB provides uncertainty quantification by sampling from a high dimensional posterior distributi…
Making the cut: improved ranking and selection for large-scale inference
Nicholas C. Henderson, Michael A. Newton
Identifying leading measurement units from a large collection is a common inference task in various domains of large-scale inference. Testing approaches, which measure evidence aga…