168 citations · 768 across the 62 of their papers we have counts for
8 papers · 1 filter
A Deep Learning Synthetic Likelihood Approximation of a Non-stationary Spatial Model for Extreme Streamflow Forecasting
Reetam Majumder, Brian Reich
Extreme streamflow is a key indicator of flood risk, and quantifying the changes in its distribution under non-stationary climate conditions is key to mitigating the impact of floo…
A Bayesian Semiparametric Method For Estimating Causal Quantile Effects
Steven G. Xu, Shu Yang, Brian J. Reich
Standard causal inference characterizes treatment effect through averages, but the counterfactual distributions could be different in not only the central tendency but also spread…
SPQR: An R Package for Semi-Parametric Density and Quantile Regression
Steven G. Xu, Reetam Majumder, Brian J. Reich
We develop an R package SPQR that implements the semi-parametric quantile regression (SPQR) method in Xu and Reich (2021). The method begins by fitting a flexible density regressio…
Matching Estimators of Causal Effects in Clustered Observational Studies with Application to Quantifying the Impact of Marine Protected Areas on Biodiversity
Can Cui, Shu Yang, Brian J Reich +1
Marine conservation preserves fish biodiversity, protects marine and coastal ecosystems, and supports climate resilience and adaptation. Despite the importance of establishing mari…
A Gaussian-process approximation to a spatial SIR process using moment closures and emulators
Parker Trostle, Joseph Guinness, Brian J. Reich
The dynamics that govern disease spread are hard to model because infections are functions of both the underlying pathogen as well as human or animal behavior. This challenge is in…
Modeling Extremal Streamflow using Deep Learning Approximations and a Flexible Spatial Process
Reetam Majumder, Brian J. Reich, Benjamin A. Shaby
Quantifying changes in the probability and magnitude of extreme flooding events is key to mitigating their impacts. While hydrodynamic data are inherently spatially dependent, trad…