Distributed Model Building and Recursive Integration for Modeling Big Spatial Data

Motivated by the important need for computationally tractable statistical methods in high dimensional spatial settings, we develop a distributed and integrated framework for estimation and inference of Gaussian model parameters with ultra-high-dimensional likelihoods. We propose a paradigm shift from whole to local data perspectives that is rooted in distributed model building and integrated estimation and inference. The framework’s backbone is a computationally and statistically efficient integration procedure that simultaneously incorporates dependence within and between spatial resolutions in a recursively partitioned spatial domain. Statistical and computational properties of our distributed approach are investigated theoretically and in simulations. The proposed approach is used to extract new insights on autism spectrum disorder from the Autism Brain Imaging Data Exchange.

Please join the event.

About Emily Hector

Emily is an Assistant Professor of Statistics at North Carolina State University. She obtained her PhD in Biostatistics from the University of Michigan in 2020, after which she joined NCSU. Her research interests are in distributed estimation and inference with applications in data integration and divide-and-conquer. Her recent work has developed methodology for the analysis of neuroimaging and wearable device data. She currently serves as an Associate Editor for Reproducibility with the Journal of the American Statistical Association. She is originally from Ottawa, and obtained her undergraduate degree from McGill University.