Ian Dryden (University of Nottingham) – Object data analysis and environmental monitoring

Object oriented data analysis (OODA) provides a framework for the statistical analysis of a wide variety of highly structured data. Shapes, images, functions, covariance matrices, networks and other manifold-valued data are all examples of object data that can be analyzed using OODA methods, and colleagues at Leeds have been at the forefront of many methodological developments. One of the tasks in OODA is regression analysis for manifold-valued data, and we consider an approach to manifold regression using intermediate spaces, by mapping from complex response or predictor manifolds into simpler spaces where statistical analysis is more straightforward. We focus on two applications: compositional data analysis of environmental chemicals in South Florida, and the monitoring of peat bogs from satellite images in the Flow Country in northern Scotland.