Speaker: Aritra Halder, Drexel University, "Bayesian Modeling with Spatial Curvature Processes"

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 Meuse river data; temperature data from the Northeastern United States; and Boston Housing data.

About the speaker: Aritra Halder is an Assistant Professor in the Department of 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 Spatio-temporal analysis, and Optimization. His areas of applications include public health and policy, hydrology, climatology, computer vision and transcriptomics.