203 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- E. T. Tchetgen Tchetgen8 · h 47
- M. J. van der Laan2 profiles6 · h 64
- T. Cai2 profiles6 · h 70
- J. Robins5 · h 115
- Daniela M. Witten2 profiles4 · h 46
- P. Hoff4 · h 36
- R. Mukherjee4 · h 23
- T. VanderWeele4 · h 104
- Anna Neufeld2 profiles3 · h 7
- A. Shojaie3 · h 37
- A. Volfovsky3 · h 16
- D. Spiegelman2 profiles3 · h 162
- Harvard UniversityUS35 papers
- University of WashingtonUS23 papers
- University of California, BerkeleyUS8 papers
- Fred Hutch Cancer CenterUS7 papers
- University of PennsylvaniaUS7 papers
- Stanford UniversityUS4 papers
- Boston UniversityUS3 papers
- Brigham and Women's HospitalUS3 papers
- Columbia UniversityUS3 papers
- Dana-Farber Cancer InstituteUS3 papers
- Hebrew University of JerusalemIL3 papers
- National Institutes of HealthUS3 papers
6 papers · 1 filter
Behavior of prediction performance metrics with rare events
Emily Minus, R. Yates Coley, Susan M. Shortreed +1
Objective: Area under the receiving operator characteristic curve (AUC) is commonly reported alongside prediction models for binary outcomes. Recent articles have raised concerns t…
Covariate-informed Representation Learning to Prevent Posterior Collapse of iVAE
Young-geun Kim, Ying Liu, Xuexin Wei
The recently proposed identifiable variational autoencoder (iVAE) framework provides a promising approach for learning latent independent components (ICs). iVAEs use auxiliary cova…
Post-Contextual-Bandit Inference
Aurélien Bibaut, Antoine Chambaz, Maria Dimakopoulou +2
Contextual bandit algorithms are increasingly replacing non-adaptive A/B tests in e-commerce, healthcare, and policymaking because they can both improve outcomes for study particip…
Integrating Additional Knowledge Into Estimation of Graphical Models
Yunqi Bu, Johannes Lederer
In applications of graphical models, we typically have more information than just the samples themselves. A prime example is the estimation of brain connectivity networks based on…
On Estimating Many Means, Selection Bias, and the Bootstrap
Noah Simon, Richard Simon
With recent advances in high throughput technology, researchers often find themselves running a large number of hypothesis tests (thousands+) and esti- mating a large number of eff…
Penalized model-based clustering with cluster-specific diagonal covariance matrices and grouped variables
Benhuai Xie, Wei Pan, Xiaotong Shen
Clustering analysis is one of the most widely used statistical tools in many emerging areas such as microarray data analysis. For microarray and other high-dimensional data, the pr…