B1517
Title: Bayesian shape analysis via the projected normal distribution
Authors: Ramses Mena - Universidad Nacional Autonoma De Mexico (Mexico) [presenting]
Abstract: A Bayesian predictive approach to statistical shape analysis is presented. A modelling strategy that starts with a Gaussian distribution on the configuration space and removes the effects of location, rotation and scale is presented. This boils down to an application of the projected normal distribution to model the configurations in the shape space, which, together with certain identifiability constraints, facilitates parameter interpretation. Having better control over the parameters allows for the generalization of the model to a regression setting where the effect of predictors on shapes can be considered. The methodology is illustrated and tested using both simulated scenarios and a real data set concerning eight anatomical landmarks on a sagittal plane of the corpus callosum in patients with autism and in a group of controls.
