284 citations · 806 across the 87 of their papers we have counts for
94 papers
Rate-Preserving Shrinking-Support Gaussian Process Prediction
Xiaopeng Xiang, Wenlin Dai, Marc G. Genton +1
Gaussian process prediction is a central tool in spatial statistics, but standard implementations require dense matrix operations that become prohibitive for large datasets. We pro…
Fisher Scoring for Exact Matérn Covariance Estimation through Stable Smoothness Optimization
Yiping Hong, Sameh Abdulah, Marc G. Genton +1
Gaussian Random Fields (GRFs) with Matérn covariance functions have emerged as a powerful framework for modeling spatial processes due to their flexibility in capturing different f…
Quantile-based causal inference for spatio-temporal processes: Assessing the impacts of wildfires on US air quality
Zipei Geng, Jordan Richards, Raphael Huser +1
Wildfires pose an increasingly severe threat to air quality, yet quantifying their causal impact remains challenging due to unmeasured meteorological and geographic confounders. Mo…
Uniform-over-dimension location tests for multivariate and high-dimensional data
Ritabrata Karmakar, Joydeep Chowdhury, Subhajit Dutta +1
Asymptotic methods for hypothesis testing in high-dimensional data usually require the dimension of the observations to increase to infinity, often with an additional relationship…
Scalable Asynchronous Federated Modeling for Spatial Data
Jianwei Shi, Sameh Abdulah, Ying Sun +1
Spatial data are central to applications such as environmental monitoring and urban planning, but are often distributed across devices where privacy and communication constraints l…
High-Performance Statistical Computing (HPSC): Challenges, Opportunities, and Future Directions
Sameh Abdulah, Mary Lai O. Salvana, Ying Sun +2
We recognize the emergence of a statistical computing community focused on working with large computing platforms and producing software and applications that exemplify high-perfor…