Modern Advances in Statistical Analysis of Spatial and Spatiotemporal Data
Category: International Statistical Institute
The boom of modern data recording systems has strewn spatially and temporally referenced data into the scientific research domain. Such data presents challenges that include—complex structure and dimensionality, dependence and computation. Solutions require novel methodological and computational frameworks to be developed. Research pursuits espouse computationally efficient Bayesian methodologies strengthening the connection with the real-world applications. This session is dedicated to bringing some of the best researchers working in the frontiers of such development within the domain of spatial and spatiotemporal modeling and analysis to the audience. The talks presented include Bayesian modeling and inference for large scale spatiotemporal data leveraging mechanistic systems within state space models, data sketching methods to overcome challenges faced in devising Bayesian computation for spatial data, boundary analysis frameworks that leverage local geometric properties for spatial surfaces, statistical inference for Gaussian processes on compact Riemannian manifolds and Bayesian analysis and inferential frameworks for age-specific event data to obtain better posterior inference regarding age-standardized estimates.