most citedSevere flooding and cause-specific hospitalization in the United States

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

collaborators

5 papers

stat.AP2024

Small area estimation of forest biomass via a two-stage model for continuous zero-inflated data

Grayson W. White, Josh K. Yamamoto, Dinan H. Elsyad +6

The United States (US) Forest Inventory & Analysis Program (FIA) collects data on and monitors the trends of forests in the US. FIA is increasingly interested in monitoring forest…

math.ST2024

Z-estimation system: a modular approach to asymptotic analysis

Jie Kate Hu

Asymptotic analysis for related inference problems often involves similar steps and proofs. These intermediate results could be shared across problems if each of them is made self-…

cs.LG2023

SpaCE: The Spatial Confounding Environment

Mauricio Tec, Ana Trisovic, Michelle Audirac +4

Spatial confounding poses a significant challenge in scientific studies involving spatial data, where unobserved spatial variables can influence both treatment and outcome, possibl…

stat.AP20232 cited

Severe flooding and cause-specific hospitalization in the United States

Sarika Aggarwal, Jie K. Hu, Jonathan A. Sullivan +2

Flooding is one of the most disruptive and costliest climate-related disasters and presents an escalating threat to population health due to climate change and urbanization pattern…

stat.ME2023

A Bayesian Nonparametric Method to Adjust for Unmeasured Confounding with Negative Controls

Jie Kate Hu, Dafne Zorzetto, Francesca Dominici

Unmeasured confounding bias threatens the validity of observational studies. While sensitivity analyses and study designs have been proposed to address this issue, they often overl…