June 6, 2024 Webinar
Bayesian Modeling with Spatial Curvature Processes
Aritra Halder, PhD
Abstract:
Spatial process models are widely used for modeling point-referenced variables arising from diverse scientific domains. Analyzing the resulting random surface provides deeper insights into the nature of latent dependence within the studied response. We develop Bayesian modeling and inference for rapid changes on the response surface to assess directional curvature along a given trajectory. Such trajectories or curves of rapid change, often referred to as wombling boundaries, occur in geographic space in the form of rivers in a flood plain, roads, mountains or plateaus or other topographic features leading to high gradients on the response surface. We demonstrate fully model based Bayesian inference on directional curvature processes to analyze differential behavior in responses along wombling boundaries. We illustrate our methodology with a number of simulated experiments followed by multiple applications featuring the Boston Housing data; Meuse river data; and temperature data from the Northeastern United States.
Short Bio:
Dr. Aritra Halder is an assistant professor of Biostatistics in the Department of Epidemiology and Biostatistics, at the Dornsife School of Public Health, Drexel University. He completed his PhD in Statistics from the University of Connecticut in July 2020. His research interests are Bayesian modeling, Spatial and Spatiotemporal analysis, and Optimization.