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20212024
most citedTree-informed Bayesian multi-source domain adaptation: cross-population probabilistic cause-of-death assignment using verbal autopsy

1 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

stat.AP2024

Bayesian analysis of verbal autopsy data using factor models with age- and sex-dependent associations between symptoms

Tsuyoshi Kunihama, Zehang Richard Li, Samuel J. Clark +1

Verbal autopsies (VAs) are extensively used to investigate the population-level distributions of deaths by cause in low-resource settings without well-organized vital statistics sy…

stat.AP2024

Treatment Effect Estimation Amidst Dynamic Network Interference in Online Gaming Experiments

Yu Zhu, Zehang Richard Li, Yang Su +1

The evolving landscape of online multiplayer gaming presents unique challenges in assessing the causal impacts of game features. Traditional A/B testing methodologies fall short du…

q-fin.PR20231 cited

Approximation of supply curves

Andres M. Alonso, Zehang Li

In this note, we illustrate the computation of the approximation of the supply curves using a one-step basis. We derive the expression for the L2 approximation and propose a proced…

stat.AP2023

Bayesian Active Questionnaire Design for Cause-of-Death Assignment Using Verbal Autopsies

Toshiya Yoshida, Trinity Shuxian Fan, Tyler McCormick +2

Only about one-third of the deaths worldwide are assigned a medically-certified cause, and understanding the causes of deaths occurring outside of medical facilities is logisticall…

stat.ME20211 cited

Tree-informed Bayesian multi-source domain adaptation: cross-population probabilistic cause-of-death assignment using verbal autopsy

Zhenke Wu, Zehang Richard Li, Irena Chen +1

Determining causes of deaths (COD) occurred outside of civil registration and vital statistics systems is challenging. A technique called verbal autopsy (VA) is widely adopted to g…