7 papers
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…
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…
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…
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…
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…