18 citations · 40 across the 14 of their papers we have counts for
4 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…
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…
Stochastic Precipitation Generation for the Chesapeake Bay Watershed using Hidden Markov Models with Variational Bayes Parameter Estimation
Reetam Majumder, Nagaraj K. Neerchal, Amita Mehta
Stochastic precipitation generators (SPGs) are a class of statistical models which generate synthetic data that can simulate dry and wet rainfall stretches for long durations. Gene…
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…