18 citations · 40 across the 14 of their papers we have counts for
16 papers
A Geometric Framework for the Bayesian Estimation of Joint Environmental Extremes
Reetam Majumder, Callum J. R. Murphy-Barltrop
Multivariate extreme events tend to have broader impacts on society compared to individual extremes. Flexible and accurate modeling of multivariate extremes is therefore crucial fo…
Quantifying Precision in Measurement of Individualized, Evidence-Based Learning Across Sessions in Applied Behavior Analysis Therapy
Mark Louie F. Ramos, Reetam Majumder, Ji Young Kim +1
Current performance criteria used in Applied Behavior Analysis (ABA) are often based on whether a learner meets a predetermined benchmark e.g., 8 out of 10 correct responses across…
Spatial Extremes at Scale: A Case Study of Surface Skin Temperature and Heat Risk in the United States
Ben Seiyon Lee, Reetam Majumder, Jordan Richards +2
Understanding and mapping extreme heat is critical for risk management and public health planning, particularly in regions with complex terrain and heterogeneous climate. We presen…
A new mixture model for spatiotemporal exceedances with flexible tail dependence
Ryan Li, Emily C. Hector, Brian J. Reich +1
We propose a new model and estimation framework for spatiotemporal streamflow exceedances above a threshold that flexibly captures asymptotic dependence and independence in the tai…
Causal Spatial Quantile Regression
Yan Gong, Reetam Majumder, Brian J. Reich +1
Treatment effects in a wide range of economic, environmental, and epidemiological applications often vary across space, and understanding the heterogeneity of causal effects across…
Semi-parametric bulk and tail regression using spline-based neural networks
Reetam Majumder, Jordan Richards
Semi-parametric quantile regression (SPQR) is a flexible approach to density regression that learns a spline-based representation of conditional density functions using neural netw…
Amortized Bayesian Local Interpolation NetworK: Fast covariance parameter estimation for Gaussian Processes
Brandon R. Feng, Reetam Majumder, Brian J. Reich +1
Gaussian processes (GPs) are a ubiquitous tool for geostatistical modeling with high levels of flexibility and interpretability, and the ability to make predictions at unseen spati…
Density correction for multivariate spatial fields of global climate model output using deep learning
Reetam Majumder, Shiqi Fang, A. Sankarasubramanian +2
Global Climate Models (GCMs) are numerical models that simulate complex physical processes within the Earth's climate system and are essential for understanding and predicting clim…
Optimal Stock Portfolio Selection with a Multivariate Hidden Markov Model
Reetam Majumder, Qing Ji, Nagaraj K. Neerchal
The underlying market trends that drive stock price fluctuations are often referred to in terms of bull and bear markets. Optimal stock portfolio selection methods need to take int…
Stochastic Gradient MCMC for Massive Geostatistical Data
Mohamed A. Abba, Brian J. Reich, Reetam Majumder +1
Gaussian processes (GPs) are commonly used for prediction and inference for spatial data analyses. However, since estimation and prediction tasks have cubic time and quadratic memo…
Deep Learning of Multivariate Extremes via a Geometric Representation
Callum J. R. Murphy-Barltrop, Reetam Majumder, Jordan Richards
The study of geometric extremes, where extremal dependence properties are inferred from the deterministic limiting shapes of scaled sample clouds, provides an exciting approach to…
Semiparametric Estimation of the Shape of the Limiting Bivariate Point Cloud
Reetam Majumder, Benjamin A. Shaby, Brian J. Reich +1
We propose a model to flexibly estimate joint tail properties by exploiting the convergence of an appropriately scaled point cloud onto a compact limit set. Characteristics of the…
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…