2.2k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Rutgers, The State University of New JerseyUS267 papers
- Princeton UniversityUS13 papers
- University of California, BerkeleyUS11 papers
- Brookhaven National LaboratoryUS10 papers
- Lawrence Berkeley National LaboratoryUS10 papers
- University of OxfordGB10 papers
- University of PennsylvaniaUS10 papers
- Centre National de la Recherche ScientifiqueFR9 papers
- Cornell UniversityUS9 papers
- Massachusetts Institute of TechnologyUS9 papers
- Stanford UniversityUS9 papers
- The University of Texas at AustinUS9 papers
9 papers · 1 filter
Bayesian Sensitivity Analysis for Causal Estimation with Time-varying Unmeasured Confounding
Yushu Zou, Liangyuan Hu, Amanda Ricciuto +2
Causal inference relies on the untestable assumption of no unmeasured confounding. Sensitivity analysis can be used to quantify the impact of unmeasured confounding on causal estim…
Incorporating Local Step-Size Adaptivity into the No-U-Turn Sampler using Gibbs Self Tuning
Nawaf Bou-Rabee, Bob Carpenter, Tore Selland Kleppe +1
Adapting the step size locally in the no-U-turn sampler (NUTS) is challenging because the step-size and path-length tuning parameters are interdependent. The determination of an op…
Bayesian Semiparametric Model for Sequential Treatment Decisions with Informative Timing
Arman Oganisian, Kelly D. Getz, Todd A. Alonzo +2
We develop a Bayesian semi-parametric model for the estimating the impact of dynamic treatment rules on survival among patients diagnosed with pediatric acute myeloid leukemia (AML…
Methods for Sparse and Low-Rank Recovery under Simplex Constraints
Ping Li, Syama Sundar Rangapuram, Martin Slawski
The de-facto standard approach of promoting sparsity by means of -regularization becomes ineffective in the presence of simplex constraints, i.e.,~the target is known to ha…
Group-Linear Empirical Bayes Estimates for a Heteroscedastic Normal Mean
Asaf Weinstein, Zhuang Ma, Lawrence D. Brown +1
The problem of estimating the mean of a normal vector with known but unequal variances introduces substantial difficulties that impair the adequacy of traditional empirical Bayes e…
Binary and Multi-Bit Coding for Stable Random Projections
Ping Li
We develop efficient binary (i.e., 1-bit) and multi-bit coding schemes for estimating the scale parameter of -stable distributions. The work is motivated by the recent work on o…
One Scan 1-Bit Compressed Sensing
Ping Li
Based on -stable random projections with small , we develop a simple algorithm for compressed sensing (sparse signal recovery) by utilizing only the signs (i.e., 1-bit) of th…
Sparse Recovery with Very Sparse Compressed Counting
Ping Li, Cun-Hui Zhang, Tong Zhang
Compressed sensing (sparse signal recovery) often encounters nonnegative data (e.g., images). Recently we developed the methodology of using (dense) Compressed Counting for recover…
Semi-Penalized Inference with Direct False Discovery Rate Control in High-Dimensions
Jian Huang, Shuangge Ma, Cun-Hui Zhang +1
We propose a new method, semi-penalized inference with direct false discovery rate control (SPIDR), for variable selection and confidence interval construction in high-dimensional…